{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "13142572",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/home/sczc619/run/LML/anaconda3/envs/tsne/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import os\n",
    "import random as rd\n",
    "import numpy as np\n",
    "import copy\n",
    "import copy as cp\n",
    "import dgl\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import scipy.sparse as sp\n",
    "from scipy.io import loadmat\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "from torch.nn import TransformerEncoder, TransformerEncoderLayer\n",
    "from torch_geometric.utils import to_dense_adj, subgraph\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score, accuracy_score, recall_score, roc_auc_score, average_precision_score, \\\n",
    "    confusion_matrix\n",
    "\n",
    "filelist = {\n",
    "    'amz_upu': 'amz_upu_adjlists.pickle',\n",
    "    'amz_usu': 'amz_usu_adjlists.pickle',\n",
    "    'amz_uvu': 'amz_uvu_adjlists.pickle',\n",
    "    'yelp_rsr': 'yelp_rsr_adjlists.pickle',\n",
    "    'yelp_rtr': 'yelp_rtr_adjlists.pickle',\n",
    "    'yelp_rur': 'yelp_rur_adjlists.pickle'\n",
    "}\n",
    "\n",
    "file_matrix_prefix = {\n",
    "    'amz_upu': 'amazon_upu_matrix_',\n",
    "    'amz_usu': 'amazon_usu_matrix_',\n",
    "    'amz_uvu': 'amazon_uvu_matrix_',\n",
    "    'yelp_rsr': 'yelpnet_rsr_matrix_decompision_',\n",
    "    'yelp_rtr': 'yelpnet_rtr_matrix_decompision_',\n",
    "    'yelp_rur': 'yelpnet_rur_matrix_decompision_'\n",
    "}\n",
    "\n",
    "\n",
    "def create_node_subgraph(node_idx, feat_data, edge_indexs, device):\n",
    "    \"\"\"\n",
    "    为单个节点创建一阶邻居子图（所有邻居）\n",
    "    \"\"\"\n",
    "    neighbors = set()\n",
    "    for rel_idx in range(len(edge_indexs)):\n",
    "        edge_index = edge_indexs[rel_idx][0].cpu().numpy()\n",
    "        # 找出以中心点为起点的边的终点\n",
    "        rel_neighbors = edge_index[1][edge_index[0] == node_idx].tolist()\n",
    "        neighbors.update(rel_neighbors)\n",
    "    \n",
    "    # 移除中心节点自身\n",
    "    neighbors.discard(node_idx)\n",
    "    neighbors = list(neighbors)\n",
    "    \n",
    "    \n",
    "    # 如果邻居太多，进行随机采样截取\n",
    "    sample_size = 399\n",
    "    if len(neighbors) > sample_size:\n",
    "        neighbors = np.random.choice(neighbors, size=sample_size, replace=False).tolist()\n",
    "    \n",
    "    \n",
    "    # 构建子图节点列表，确保中心节点是第一个\n",
    "    sub_nodes = [node_idx] + [n for n in neighbors if n != node_idx]\n",
    "    \n",
    "    # 构建子图边列表\n",
    "    sub_edge_index = []\n",
    "    for rel_idx in range(len(edge_indexs)):\n",
    "        edge_index = edge_indexs[rel_idx][0].cpu().numpy()\n",
    "        mask = np.isin(edge_index[0], sub_nodes) & np.isin(edge_index[1], sub_nodes)\n",
    "        local_edges = edge_index[:, mask]\n",
    "\n",
    "        # 创建节点映射\n",
    "        node_map = {n: i for i, n in enumerate(sub_nodes)}\n",
    "\n",
    "        # 将全局索引映射到局部索引\n",
    "        if len(local_edges) > 0 and local_edges.size > 0:\n",
    "            src_nodes = [node_map[src] for src in local_edges[0]]\n",
    "            dst_nodes = [node_map[dst] for dst in local_edges[1]]\n",
    "            edge_tensor = torch.tensor([src_nodes, dst_nodes], dtype=torch.long)\n",
    "        else:\n",
    "            # 添加自环确保图不为空\n",
    "            edge_tensor = torch.tensor([[0], [0]], dtype=torch.long)\n",
    "\n",
    "        sub_edge_index.append(edge_tensor.to(device))\n",
    "\n",
    "    # 创建子图数据\n",
    "    subgraph = {\n",
    "        'features': feat_data[sub_nodes].clone(),\n",
    "        'edges': sub_edge_index,\n",
    "        'global_idx': sub_nodes\n",
    "    }\n",
    "\n",
    "    return subgraph\n",
    "\n",
    "\n",
    "def dict_to_edge_index(edge_dict):\n",
    "    source_nodes = []\n",
    "    target_nodes = []\n",
    "    for src, targets in edge_dict.items():\n",
    "        for target in targets:\n",
    "            source_nodes.append(src)\n",
    "            target_nodes.append(target)\n",
    "    edge_index = [source_nodes, target_nodes]\n",
    "    return torch.LongTensor(edge_index)\n",
    "\n",
    "\n",
    "def numpy_array_to_edge_index(np_array):\n",
    "    assert np_array.ndim == 2 and np_array.shape[0] == np_array.shape[1], \"Input must be a square matrix.\"\n",
    "    rows, cols = np.nonzero(np_array)\n",
    "    edge_index = np.vstack((rows, cols))\n",
    "    edge_index_tensor = torch.from_numpy(edge_index).long()\n",
    "    return edge_index_tensor\n",
    "\n",
    "\n",
    "def load_data(data, k=2, prefix=''):\n",
    "    pickle_file = {}\n",
    "    matrix_prefix = {}\n",
    "    for key in filelist:\n",
    "        pickle_file[key] = os.path.join(prefix, filelist[key])\n",
    "        matrix_prefix[key] = os.path.join(prefix, file_matrix_prefix[key])\n",
    "\n",
    "    if data == 'yelp':\n",
    "        data_file = loadmat(os.path.join(prefix, 'YelpChi.mat'))\n",
    "        labels = data_file['label'].flatten()\n",
    "        feat_data = data_file['features'].todense().A\n",
    "        with open(pickle_file['yelp_rur'], 'rb') as file:\n",
    "            relation1 = pickle.load(file)\n",
    "        file.close()\n",
    "        relation1 = dict_to_edge_index(relation1)\n",
    "        relation1_tree = []\n",
    "        for i in range(1, k + 1):\n",
    "            file_name = '{}{}.pkl'.format(matrix_prefix['yelp_rur'], i)\n",
    "            with open(file_name, 'rb') as file:\n",
    "                tree = pickle.load(file)\n",
    "            file.close()\n",
    "            relation1_tree.append(numpy_array_to_edge_index(tree))\n",
    "        with open(pickle_file['yelp_rtr'], 'rb') as file:\n",
    "            relation2 = pickle.load(file)\n",
    "        file.close()\n",
    "        relation2 = dict_to_edge_index(relation2)\n",
    "        relation2_tree = []\n",
    "        for i in range(1, k + 1):\n",
    "            file_name = '{}{}.pkl'.format(matrix_prefix['yelp_rtr'], i)\n",
    "            with open(file_name, 'rb') as file:\n",
    "                tree = pickle.load(file)\n",
    "            file.close()\n",
    "            relation2_tree.append(numpy_array_to_edge_index(tree))\n",
    "        with open(pickle_file['yelp_rsr'], 'rb') as file:\n",
    "            relation3 = pickle.load(file)\n",
    "        file.close()\n",
    "        relation3 = dict_to_edge_index(relation3)\n",
    "        relation3_tree = []\n",
    "        for i in range(1, k + 1):\n",
    "            file_name = '{}{}.pkl'.format(matrix_prefix['yelp_rsr'], i)\n",
    "            with open(file_name, 'rb') as file:\n",
    "                tree = pickle.load(file)\n",
    "            file.close()\n",
    "            relation3_tree.append(numpy_array_to_edge_index(tree))\n",
    "        return [[relation1, relation1_tree], [relation2, relation2_tree],\n",
    "                [relation3, relation3_tree]], feat_data, labels\n",
    "    elif data == 'amazon':\n",
    "        data_file = loadmat(os.path.join(prefix, 'Amazon.mat'))\n",
    "        labels = data_file['label'].flatten()\n",
    "        feat_data = data_file['features'].todense().A\n",
    "        with open(pickle_file['amz_upu'], 'rb') as file:\n",
    "            relation1 = pickle.load(file)\n",
    "        file.close()\n",
    "        relation1 = dict_to_edge_index(relation1)\n",
    "        relation1_tree = []\n",
    "        for i in range(1, k + 1):\n",
    "            file_name = '{}{}.pkl'.format(matrix_prefix['amz_upu'], i)\n",
    "            with open(file_name, 'rb') as file:\n",
    "                tree = pickle.load(file)\n",
    "            file.close()\n",
    "            relation1_tree.append(numpy_array_to_edge_index(tree))\n",
    "        with open(pickle_file['amz_usu'], 'rb') as file:\n",
    "            relation2 = pickle.load(file)\n",
    "        file.close()\n",
    "        relation2 = dict_to_edge_index(relation2)\n",
    "        relation2_tree = []\n",
    "        for i in range(1, k + 1):\n",
    "            file_name = '{}{}.pkl'.format(matrix_prefix['amz_usu'], i)\n",
    "            with open(file_name, 'rb') as file:\n",
    "                tree = pickle.load(file)\n",
    "            file.close()\n",
    "            relation2_tree.append(numpy_array_to_edge_index(tree))\n",
    "        with open(pickle_file['amz_uvu'], 'rb') as file:\n",
    "            relation3 = pickle.load(file)\n",
    "        file.close()\n",
    "        relation3_tree = []\n",
    "        for i in range(1, k + 1):\n",
    "            file_name = '{}{}.pkl'.format(matrix_prefix['amz_uvu'], i)\n",
    "            with open(file_name, 'rb') as file:\n",
    "                tree = pickle.load(file)\n",
    "            file.close()\n",
    "            relation3_tree.append(numpy_array_to_edge_index(tree))\n",
    "        relation3 = dict_to_edge_index(relation3)\n",
    "\n",
    "        return [[relation1, relation1_tree], [relation2, relation2_tree],\n",
    "                [relation3, relation3_tree]], feat_data, labels\n",
    "\n",
    "\n",
    "def Visualization(labels, embedding, prefix):\n",
    "    train_pos, train_neg = pos_neg_split(list(range(len(labels))), labels)\n",
    "    sampled_idx_train = undersample(train_pos, train_neg, scale=1)\n",
    "    tsne = TSNE(n_components=2, random_state=43)\n",
    "    sampled_idx_train = np.array(sampled_idx_train)\n",
    "    sampled_idx_train = np.random.choice(sampled_idx_train, size=5000, replace=True)\n",
    "    ps = embedding[sampled_idx_train]\n",
    "    ls = labels[sampled_idx_train]\n",
    "\n",
    "    X_reduced = tsne.fit_transform(ps)\n",
    "\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    X_scaled = scaler.fit_transform(X_reduced)\n",
    "    print(X_scaled.shape)\n",
    "\n",
    "    plt.figure(figsize=(8, 8))\n",
    "\n",
    "    plt.scatter(X_scaled[ls == 0, 0], X_scaled[ls == 0, 1], c='#14517C', label='Label 0', s=3)\n",
    "\n",
    "    plt.scatter(X_scaled[ls == 1, 0], X_scaled[ls == 1, 1], c='#FA7F6F', label='Label 1', s=3)\n",
    "\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.xlim(0, 1)\n",
    "    plt.ylim(0, 1)\n",
    "    filepath = os.path.join(prefix, 'HOGRL.png')\n",
    "    plt.savefig(filepath)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def normalize(mx):\n",
    "    rowsum = np.array(mx.sum(1)) + 0.01\n",
    "    r_inv = np.power(rowsum, -1).flatten()\n",
    "    r_inv[np.isinf(r_inv)] = 0.\n",
    "    r_mat_inv = sp.diags(r_inv)\n",
    "    mx = r_mat_inv.dot(mx)\n",
    "    return mx\n",
    "\n",
    "\n",
    "def pos_neg_split(nodes, labels):\n",
    "    # 正负样本分割\n",
    "    pos_nodes = []\n",
    "    neg_nodes = cp.deepcopy(nodes)\n",
    "    aux_nodes = cp.deepcopy(nodes)\n",
    "    for idx, label in enumerate(labels):\n",
    "        if label == 1:\n",
    "            pos_nodes.append(aux_nodes[idx])\n",
    "            neg_nodes.remove(aux_nodes[idx])\n",
    "\n",
    "    return pos_nodes, neg_nodes\n",
    "\n",
    "\n",
    "def undersample(pos_nodes, neg_nodes, scale=1):\n",
    "    # 对负样本进行下采样，平衡正负样本数量\n",
    "    aux_nodes = cp.deepcopy(neg_nodes)\n",
    "    aux_nodes = rd.sample(aux_nodes, k=int(len(pos_nodes) * scale))\n",
    "    batch_nodes = pos_nodes + aux_nodes\n",
    "\n",
    "    return batch_nodes\n",
    "\n",
    "\n",
    "def calculate_g_mean(y_true, y_pred):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    sensitivities = []\n",
    "    for i in range(len(cm)):\n",
    "        TP = cm[i, i]\n",
    "        FN = cm[i, :].sum() - TP\n",
    "        sensitivity = TP / (TP + FN) if (TP + FN) != 0 else 0\n",
    "        sensitivities.append(sensitivity)\n",
    "    g_mean = np.prod(sensitivities) ** (1 / len(sensitivities))\n",
    "    return g_mean\n",
    "\n",
    "\n",
    "def iterate_batches(indices, batch_size, shuffle=True):\n",
    "    \"\"\"\n",
    "        将索引列表划分为指定大小的批次\n",
    "        :param indices: 样本索引列表\n",
    "        :param batch_size: 每个批次的大小\n",
    "        :param shuffle: 是否打乱顺序\n",
    "        :return: 生成批次索引的迭代器\n",
    "    \"\"\"\n",
    "    if shuffle:\n",
    "        rd.shuffle(indices)\n",
    "    for i in range(0, len(indices), batch_size):\n",
    "        yield indices[i:i + batch_size]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def test(idx_eval, y_eval, model, feat_data, edge_indexs, device, batch_size=64):\n",
    "    model.eval()\n",
    "    all_probs = []\n",
    "    all_labels = []\n",
    "\n",
    "    # 分批处理\n",
    "    for batch_centers in iterate_batches(idx_eval, batch_size, shuffle=False):\n",
    "        subgraph_data = []\n",
    "\n",
    "        # 为每个中心节点构建子图\n",
    "        for xi in batch_centers:\n",
    "            subgraph = create_node_subgraph(xi, feat_data, edge_indexs, device)\n",
    "            subgraph_data.append(subgraph)\n",
    "\n",
    "        # 获取中心节点预测\n",
    "        with torch.no_grad():\n",
    "            center_logits, _ = model(subgraph_data)  # [B, 2]\n",
    "            probs = torch.softmax(center_logits, dim=-1)[:, 1]  # 正类概率\n",
    "            all_probs.extend(probs.cpu().numpy())\n",
    "            all_labels.extend([labels[xi] for xi in batch_centers])\n",
    "\n",
    "    # 计算指标\n",
    "    auc_score = roc_auc_score(all_labels, all_probs)\n",
    "    ap_score = average_precision_score(all_labels, all_probs)\n",
    "    pred_labels = (np.array(all_probs) >= 0.5).astype(int)\n",
    "    f1 = f1_score(all_labels, pred_labels, average='macro')\n",
    "    g_mean = calculate_g_mean(all_labels, pred_labels)\n",
    "\n",
    "    return auc_score, ap_score, f1, g_mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13defdaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BSNE_Transformer(nn.Module):\n",
    "    def __init__(self, in_feat, out_feat, relation_nums=3, d_model=256,\n",
    "                 nhead=8, num_layers=3, dim_feedforward=256,\n",
    "                 drop_rate=0.5):\n",
    "        super().__init__()\n",
    "        self.relation_nums = relation_nums\n",
    "        self.d_model = d_model\n",
    "        self.nhead = nhead\n",
    "\n",
    "        self.feature_proj = nn.Sequential(\n",
    "            nn.Linear(in_feat, d_model),\n",
    "            nn.LayerNorm(d_model),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        self.norm = nn.LayerNorm(d_model)\n",
    "        \n",
    "        self.relation_encoders = nn.ModuleList([\n",
    "            TransformerEncoder(\n",
    "                TransformerEncoderLayer(\n",
    "                    d_model=d_model,\n",
    "                    nhead=nhead,\n",
    "                    dim_feedforward=dim_feedforward,\n",
    "                    dropout=drop_rate,\n",
    "                    batch_first=True\n",
    "                ),\n",
    "                num_layers=num_layers\n",
    "            ) for _ in range(relation_nums)\n",
    "        ])\n",
    "\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Linear(relation_nums * d_model, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(drop_rate),\n",
    "            nn.Linear(512, out_feat)\n",
    "        )\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:\n",
    "                nn.init.xavier_uniform_(p)\n",
    "                \n",
    "#         self.node_feature_extractor = nn.Sequential(\n",
    "#             nn.Linear(relation_nums * d_model, d_model),\n",
    "#             nn.ReLU(),\n",
    "#             nn.LayerNorm(d_model)\n",
    "#         )\n",
    "\n",
    "    def forward(self, subgraph_batch):\n",
    "        # 为每个子图单独处理\n",
    "        center_logits_list = []\n",
    "        center_features_list = []\n",
    "        \n",
    "        for sg in subgraph_batch:\n",
    "            # 处理单个子图\n",
    "            features = self.feature_proj(sg['features'].unsqueeze(0))  # [1, num_nodes, d_model]\n",
    "            features = self.norm(features)\n",
    "            \n",
    "            # 为每个关系类型单独处理\n",
    "            rel_outputs = []\n",
    "            num_nodes = features.size(1)\n",
    "            \n",
    "            for rel_idx in range(self.relation_nums):\n",
    "                # 构建当前关系的邻接矩阵\n",
    "                edge_index = sg['edges'][rel_idx]\n",
    "                adj = torch.zeros(num_nodes, num_nodes, \n",
    "                                 dtype=torch.float, device=features.device)\n",
    "                \n",
    "                if edge_index.size(1) > 0:\n",
    "                    src, dst = edge_index\n",
    "                    adj[src, dst] = 1.0\n",
    "                \n",
    "                # 添加自环\n",
    "                adj[range(num_nodes), range(num_nodes)] = 1.0\n",
    "                \n",
    "                # 创建注意力掩码\n",
    "                adj_mask = adj.masked_fill(adj == 0.0, float('-inf'))\n",
    "                adj_mask = adj_mask.masked_fill(adj == 1.0, 0.0)\n",
    "                \n",
    "                # 扩展为多头注意力掩码\n",
    "                adj_mask = adj_mask.unsqueeze(0).unsqueeze(0)  # [1, 1, num_nodes, num_nodes]\n",
    "                adj_mask = adj_mask.expand(1, self.nhead, num_nodes, num_nodes)\n",
    "                adj_mask = adj_mask.reshape(-1, num_nodes, num_nodes)\n",
    "                \n",
    "                # 关系编码\n",
    "                encoder_output = self.relation_encoders[rel_idx](\n",
    "                    src=features,\n",
    "                    mask=adj_mask\n",
    "                )\n",
    "                rel_outputs.append(encoder_output)\n",
    "            \n",
    "            # 合并多关系特征\n",
    "            combined = torch.cat(rel_outputs, dim=-1)  # [1, num_nodes, rel*d_model]\n",
    "            \n",
    "            # 提取中心节点特征（第一个节点）\n",
    "            center_features = combined[:, 0, :]  # [1, rel*d_model]\n",
    "            center_logits = self.classifier(center_features)  # [1, out_feat]\n",
    "            center_logits = F.log_softmax(center_logits, dim=-1)\n",
    "            \n",
    "            center_logits_list.append(center_logits)\n",
    "            center_features_list.append(center_features)\n",
    "        \n",
    "        # 将结果堆叠为批次\n",
    "        center_logits = torch.cat(center_logits_list, dim=0)\n",
    "        center_features = torch.cat(center_features_list, dim=0)\n",
    "        \n",
    "        return center_logits, center_features\n",
    "\n",
    "\n",
    "#     def forward(self, subgraph_batch):\n",
    "        \n",
    "#         # 计算最大节点数\n",
    "#         max_nodes = max(sg['features'].size(0) for sg in subgraph_batch)\n",
    "#         batch_size = len(subgraph_batch)\n",
    "\n",
    "#         # 特征填充和掩码生成\n",
    "#         padded_features = []\n",
    "#         center_mask = torch.zeros(batch_size, max_nodes, dtype=torch.bool)\n",
    "#         padding_masks = {rel: [] for rel in range(self.relation_nums)}\n",
    "\n",
    "#         for i, sg in enumerate(subgraph_batch):\n",
    "#             num_real_nodes = sg['features'].size(0)\n",
    "#             padded_feat = F.pad(sg['features'], (0, 0, 0, max_nodes - num_real_nodes))\n",
    "#             padded_features.append(padded_feat)\n",
    "\n",
    "#             # 标记中心节点位置（每个子图的第一个节点）\n",
    "#             center_mask[i, 0] = True\n",
    "\n",
    "#             # 生成padding mask\n",
    "#             pad_mask = torch.zeros(max_nodes, dtype=torch.bool, device=sg['features'].device)\n",
    "#             pad_mask[num_real_nodes:] = True\n",
    "#             for rel in range(self.relation_nums):\n",
    "#                 padding_masks[rel].append(pad_mask)\n",
    "\n",
    "#         # 转换为批量张量\n",
    "#         features = torch.stack(padded_features)\n",
    "#         features = self.feature_proj(features)\n",
    "#         features = self.norm(features)\n",
    "#         center_mask = center_mask.to(features.device)\n",
    "# #         print('features shape:',features.shape)\n",
    "# #         print('features:',features)\n",
    "#         # 多关系处理\n",
    "#         relation_outputs = []\n",
    "#         for rel_idx in range(self.relation_nums):\n",
    "#             adj_masks = []\n",
    "#             for sg in subgraph_batch:\n",
    "#                 edge_index = sg['edges'][rel_idx]\n",
    "#                 adj = torch.zeros(max_nodes, max_nodes, dtype=torch.float, device=features.device)\n",
    "#                 if edge_index.size(1) > 0:\n",
    "#                     src, dst = edge_index\n",
    "#                     adj[src, dst] = 1.0\n",
    "#                 # 添加自环\n",
    "#                 adj[range(len(adj)), range(len(adj))] = 1.0\n",
    "#                 adj_masks.append(adj)\n",
    "\n",
    "#             # 创建邻接矩阵mask\n",
    "#             adj_mask = torch.stack(adj_masks)\n",
    "#             adj_mask = adj_mask.masked_fill(adj_mask == 0.0, float('-inf'))\n",
    "#             adj_mask = adj_mask.masked_fill(adj_mask == 1.0, 0.0)\n",
    "\n",
    "#             # 处理padding mask\n",
    "#             src_key_padding_mask = torch.stack(padding_masks[rel_idx])\n",
    "\n",
    "#             # 合并两种mask\n",
    "#             padding_3d_mask = src_key_padding_mask.unsqueeze(1) | src_key_padding_mask.unsqueeze(2)\n",
    "#             combined_mask = adj_mask.masked_fill(padding_3d_mask, float('-inf'))\n",
    "\n",
    "#             # 扩展mask到多头\n",
    "#             combined_mask = combined_mask.unsqueeze(1)\n",
    "#             combined_mask = combined_mask.expand(-1, self.nhead, -1, -1)\n",
    "#             combined_mask = combined_mask.reshape(-1, max_nodes, max_nodes)\n",
    "            \n",
    "#             # 关系编码\n",
    "#             encoder_output = self.relation_encoders[rel_idx](\n",
    "#                 src=features,\n",
    "#                 mask=combined_mask\n",
    "#             )\n",
    "#             encoder_output = self.norm(encoder_output)\n",
    "#             relation_outputs.append(encoder_output)\n",
    "            \n",
    "# #         print('relation_outputs:', relation_outputs)\n",
    "#         # 合并多关系特征\n",
    "#         combined = torch.cat(relation_outputs, dim=-1)  # [B, max_n, rel*d_model]\n",
    "# #         print('combined:', combined.shape, combined)\n",
    "        \n",
    "#         # 中心节点分类\n",
    "#         center_features = combined[center_mask]  # [B, rel*d_model]\n",
    "#         center_logits = self.classifier(center_features)  # [B, out_feat]\n",
    "#         center_logits = F.log_softmax(center_logits, dim=-1)\n",
    "        \n",
    "        \n",
    "# #         print('center_features:', center_features)\n",
    "        \n",
    "#         # 所有节点特征提取\n",
    "# #         node_features = self.node_feature_extractor(combined)  # [B, max_n, d_model]\n",
    "        \n",
    "        \n",
    "# #         return center_logits, node_features\n",
    "#         return center_logits, center_features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a3175cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这一部分已经融入到预训练部分的代码中\n",
    "\n",
    "def calculate_tsne_loss(model, feat_data, dist_matrix, batch_centers,\n",
    "                        sample_size=20, temperature=1, eps=1e-12,\n",
    "                        edge_indexs=None, device=None):\n",
    "    \"\"\"\n",
    "    TSNE损失计算函数 - 改进版\n",
    "    按照以下步骤计算:\n",
    "    1. 随机采样batch_size个中心点\n",
    "    2. 为每个中心点构建Bp子图(基于距离概率采样)\n",
    "    3. 为每个中心点构建Bu子图(随机均匀采样)\n",
    "    4. 为Bp和Bu子图中的每个节点计算特征\n",
    "    5. 计算每对Bp和Bu子图的local_loss和global_loss\n",
    "    6. 对所有子图的loss求平均\n",
    "    \"\"\"\n",
    "#     model.eval()\n",
    "    model.train()\n",
    "    total_loss = 0.0\n",
    "    batch_size = len(batch_centers)\n",
    "\n",
    "    # 为每个中心节点构建Bp子图\n",
    "    bp_subgraphs = []\n",
    "    for xi in batch_centers:\n",
    "        # 根据距离矩阵计算采样概率\n",
    "        dist_row = dist_matrix[xi].cpu().numpy()\n",
    "        probs = np.exp(-dist_row)\n",
    "        probs[xi] = 0  # 排除自身\n",
    "        probs /= probs.sum()\n",
    "\n",
    "        # 采样邻居节点\n",
    "        available_nodes = len(dist_row) - 1\n",
    "        actual_sample_size = min(sample_size, available_nodes)\n",
    "        \n",
    "        if actual_sample_size > 0:\n",
    "            neighbors = np.random.choice(len(dist_row), size=actual_sample_size, p=probs, replace=False)\n",
    "            # 将中心节点和采样的邻居合并\n",
    "            subgraph_nodes = [xi] + neighbors.tolist()\n",
    "        else:\n",
    "            # 如果没有可用邻居，只使用中心节点\n",
    "            subgraph_nodes = [xi]\n",
    "            \n",
    "        bp_subgraphs.append(subgraph_nodes)\n",
    "\n",
    "    # 为每个中心节点构建Bu子图（均匀采样）\n",
    "    bu_subgraphs = []\n",
    "    for _ in range(batch_size):\n",
    "        # 随机采样节点构建Bu子图 (sample_size + 1个节点)\n",
    "        random_nodes = np.random.choice(feat_data.shape[0], size=sample_size + 1, replace=False)\n",
    "        bu_subgraphs.append(random_nodes.tolist())\n",
    "\n",
    "    # 计算Bp子图中所有节点的特征\n",
    "    bp_features_batch = []\n",
    "    for bp_nodes in bp_subgraphs:\n",
    "        bp_node_features = []\n",
    "        \n",
    "        # 为子图中的每个节点计算特征\n",
    "        for node_idx in bp_nodes:\n",
    "            # 构建该节点的一阶邻居子图\n",
    "            node_subgraph = create_node_subgraph(node_idx, feat_data, edge_indexs, device)\n",
    "            \n",
    "            # 获取节点特征\n",
    "            _, node_feature = model([node_subgraph])\n",
    "            \n",
    "            bp_node_features.append(node_feature.squeeze(0))\n",
    "            \n",
    "        # 将该Bp子图所有节点的特征收集起来\n",
    "        bp_features_batch.append(torch.stack(bp_node_features))  # [sample_size+1, d_model]\n",
    "\n",
    "    # 计算Bu子图中所有节点的特征\n",
    "    bu_features_batch = []\n",
    "    for bu_nodes in bu_subgraphs:\n",
    "        bu_node_features = []\n",
    "        \n",
    "        # 为子图中的每个节点计算特征\n",
    "        for node_idx in bu_nodes:\n",
    "            # 构建该节点的一阶邻居子图\n",
    "            node_subgraph = create_node_subgraph(node_idx, feat_data, edge_indexs, device)\n",
    "            \n",
    "            # 获取节点特征\n",
    "            _, node_feature = model([node_subgraph])\n",
    "            \n",
    "            bu_node_features.append(node_feature.squeeze(0))\n",
    "            \n",
    "        # 将该Bu子图所有节点的特征收集起来\n",
    "        bu_features_batch.append(torch.stack(bu_node_features))  # [sample_size+1, d_model]\n",
    "\n",
    "    # 针对每一对Bp和Bu子图计算loss\n",
    "    for i in range(batch_size):\n",
    "        bp_nodes = bp_subgraphs[i]\n",
    "        bp_features = bp_features_batch[i]  # [bp_size, d_model]\n",
    "        bu_features = bu_features_batch[i]  # [bu_size, d_model]\n",
    "        \n",
    "        # 计算Bp子图中的距离矩阵（原始图距离）\n",
    "        dist_bp = dist_matrix[bp_nodes][:, bp_nodes].cpu().numpy()\n",
    "        \n",
    "        # 计算P矩阵（基于距离）\n",
    "        P = (1.0 + dist_bp ** 2) ** -1\n",
    "        np.fill_diagonal(P, 0)  # 对角线置零\n",
    "        P = (P + P.T) / 2  # 对称化\n",
    "        row_sums = P.sum(axis=1, keepdims=True)\n",
    "        # 防止除零\n",
    "        row_sums = np.where(row_sums == 0, eps, row_sums)\n",
    "        P = P / row_sums\n",
    "        P = torch.clamp(torch.tensor(P, device=device), min=eps)\n",
    "        \n",
    "        # 计算Q矩阵（基于特征距离）\n",
    "        pairwise_dist = torch.cdist(bp_features, bp_features, p=2)  # [bp_size, bp_size]\n",
    "        Q = (1.0 + pairwise_dist ** 2 / temperature) ** -1\n",
    "        Q.fill_diagonal_(0)\n",
    "        Q = (Q + Q.permute(1, 0)) / 2\n",
    "        Q_sum = Q.sum(dim=1, keepdim=True)\n",
    "        Q_sum = torch.where(Q_sum == 0, torch.tensor(eps, device=device), Q_sum)\n",
    "        Q = Q / Q_sum\n",
    "        \n",
    "        # 局部损失\n",
    "        loss_local = (P * (torch.log(P) - torch.log(Q))).sum()\n",
    "        \n",
    "        # 全局损失\n",
    "        sum_p_sub = P.sum(dim=1)\n",
    "        N = dist_matrix.shape[0]\n",
    "        k_Bp = (sum_p_sub * N / len(bp_nodes)).mean()\n",
    "        \n",
    "        # 计算Bp子图与Bu子图之间的特征距离\n",
    "        d_bu = torch.exp(-torch.cdist(bp_features, bu_features, p=2))\n",
    "        sum_e_bu = d_bu.sum()\n",
    "        sum_e_bp = torch.exp(-pairwise_dist).sum()\n",
    "        \n",
    "        global_ratio = k_Bp * (sum_e_bu / (sum_e_bp + eps))\n",
    "        loss_global = torch.log(global_ratio.clamp(min=eps))\n",
    "        \n",
    "        # 合并局部损失和全局损失\n",
    "        total_loss += (loss_local + loss_global)\n",
    "        \n",
    "    bsne_loss = total_loss / batch_size\n",
    "    # 返回平均损失\n",
    "    return bsne_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb3dcb50",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n",
      "loading data...\n",
      "\n",
      "=== Starting Pretraining ===\n",
      "Pretrain Epoch: 000, BSNE Loss: 6.3968\n",
      "Pretrain Epoch: 005, BSNE Loss: 6.4328\n",
      "Pretrain Epoch: 010, BSNE Loss: 6.5397\n",
      "Pretrain Epoch: 015, BSNE Loss: 6.4869\n",
      "Pretrain Epoch: 020, BSNE Loss: 6.4834\n",
      "Pretrain Epoch: 025, BSNE Loss: 6.4105\n",
      "Pretrain early stopping at epoch 29\n",
      "\n",
      "=== Starting Fine-tuning ===\n",
      "Epoch: 000 | Loss: 0.0806 | Val AUC: 0.7077 | Val F1: 0.4751\n",
      "Epoch: 005 | Loss: 0.0048 | Val AUC: 0.8138 | Val F1: 0.4751\n",
      "Epoch: 010 | Loss: 0.0017 | Val AUC: 0.8311 | Val F1: 0.4751\n",
      "Epoch: 015 | Loss: 0.0047 | Val AUC: 0.8431 | Val F1: 0.4751\n",
      "Epoch: 020 | Loss: 0.0088 | Val AUC: 0.8548 | Val F1: 0.4751\n",
      "Epoch: 025 | Loss: 0.0631 | Val AUC: 0.8669 | Val F1: 0.4813\n",
      "Epoch: 030 | Loss: 0.0341 | Val AUC: 0.8724 | Val F1: 0.4813\n",
      "Epoch: 035 | Loss: 0.0379 | Val AUC: 0.8875 | Val F1: 0.4989\n",
      "Epoch: 040 | Loss: 0.0438 | Val AUC: 0.8903 | Val F1: 0.5648\n",
      "Epoch: 045 | Loss: 0.0177 | Val AUC: 0.8876 | Val F1: 0.5483\n",
      "Epoch: 050 | Loss: 0.1346 | Val AUC: 0.8869 | Val F1: 0.4977\n",
      "Epoch: 055 | Loss: 0.0237 | Val AUC: 0.8873 | Val F1: 0.6936\n",
      "Epoch: 060 | Loss: 0.0048 | Val AUC: 0.8883 | Val F1: 0.7340\n",
      "Epoch: 065 | Loss: 0.0125 | Val AUC: 0.8859 | Val F1: 0.7465\n",
      "Epoch: 070 | Loss: 0.0115 | Val AUC: 0.8842 | Val F1: 0.7413\n",
      "Epoch: 075 | Loss: 0.0945 | Val AUC: 0.8877 | Val F1: 0.7388\n",
      "Epoch: 080 | Loss: 0.0055 | Val AUC: 0.8901 | Val F1: 0.7490\n",
      "Epoch: 085 | Loss: 0.0727 | Val AUC: 0.8858 | Val F1: 0.7572\n",
      "Epoch: 090 | Loss: 0.0141 | Val AUC: 0.8904 | Val F1: 0.7593\n",
      "Epoch: 095 | Loss: 0.0521 | Val AUC: 0.8864 | Val F1: 0.7569\n",
      "Epoch 00020: reducing learning rate of group 0 to 2.5000e-04.\n",
      "\n",
      "=== Final Test Results ===\n",
      "Test AUC: 0.8848 | Test AP: 0.5083 | Test F1: 0.7528 | G-mean: 0.7297\n"
     ]
    }
   ],
   "source": [
    "# 进行完整测试\n",
    "\n",
    "# 参数设置\n",
    "args = {\n",
    "    \"dataset\": \"amazon\",\n",
    "    \"batch_size\": 8,\n",
    "    \"sample_size\": 20,\n",
    "    \"weight_decay\": 0.00005,\n",
    "    \"emb_size\": 32,\n",
    "    \"pretrain_epochs\": 100,\n",
    "    \"pretrain_lr\": 0.0001,\n",
    "    \"finetune_lr\": 0.0005,\n",
    "    \"num_epochs\": 100,\n",
    "    \"pretrain_patience\": 20,\n",
    "    \"patience\": 30,\n",
    "    \"tsne_weight\": 0.3,\n",
    "    \"weight\": 0.6,\n",
    "    \"layers\": 7,\n",
    "    \"test_size\": 0.6,\n",
    "    \"val_size\": 0.5,\n",
    "    \"layers_tree\": 7,\n",
    "    \"seed\": 76,\n",
    "    \"num_heads\": 2,\n",
    "    \"drop_rate\": 0.5\n",
    "}\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)\n",
    "\n",
    "timestamp = time.strftime(\"%Y%m%d-%H%M%S\")\n",
    "writer = SummaryWriter(f'runs/{args[\"dataset\"]}_{timestamp}')\n",
    "\n",
    "print('loading data...')\n",
    "prefix = \"../../data/\"\n",
    "\n",
    "edge_indexs, feat_data, labels = load_data(args['dataset'], args['layers_tree'], prefix)\n",
    "\n",
    "np.random.seed(args['seed'])\n",
    "rd.seed(args['seed'])\n",
    "\n",
    "if args['dataset'] == 'yelp':\n",
    "    index = list(range(len(labels)))\n",
    "    idx_train_val, idx_test, y_train_val, y_test = train_test_split(index, labels, stratify=labels,\n",
    "                                                                    test_size=args['test_size'], random_state=2,\n",
    "                                                                    shuffle=True)\n",
    "    idx_train, idx_val, y_train, y_val = train_test_split(idx_train_val, y_train_val,\n",
    "                                                          stratify=y_train_val, test_size=args['val_size'],\n",
    "                                                          random_state=2, shuffle=True)\n",
    "    dist_path = os.path.join(prefix, \"YelpChi_shortest_distance.pkl\")\n",
    "elif args['dataset'] == 'amazon':\n",
    "    index = list(range(3305, len(labels)))\n",
    "    idx_train_val, idx_test, y_train_val, y_test = train_test_split(index, labels[3305:],\n",
    "                                                                    stratify=labels[3305:],\n",
    "                                                                    test_size=args['test_size'],\n",
    "                                                                    random_state=2, shuffle=True)\n",
    "    idx_train, idx_val, y_train, y_val = train_test_split(idx_train_val, y_train_val,\n",
    "                                                          stratify=y_train_val, test_size=args['val_size'],\n",
    "                                                          random_state=2, shuffle=True)\n",
    "    dist_path = os.path.join(prefix, \"Amazon_shortest_distance.pkl\")\n",
    "\n",
    "with open(dist_path, 'rb') as f:\n",
    "    dist_data = pickle.load(f)\n",
    "    dist_matrix = torch.tensor(dist_data['dist_matrix']).to(device)\n",
    "\n",
    "# 准备特征数据\n",
    "feat_data = torch.tensor(feat_data).float()\n",
    "# 特征归一化\n",
    "scaler = MinMaxScaler()\n",
    "feat_data = torch.tensor(scaler.fit_transform(feat_data)).float().to(device)\n",
    "\n",
    "# 初始化模型\n",
    "bsne_model = BSNE_Transformer(\n",
    "    in_feat=feat_data.shape[1],\n",
    "    out_feat=2,\n",
    "    relation_nums=len(edge_indexs),\n",
    "    d_model=64,\n",
    "    nhead=args['num_heads'],\n",
    "    num_layers=3,\n",
    "    dim_feedforward=256,\n",
    "    drop_rate=args['drop_rate']\n",
    ").to(device)\n",
    "\n",
    "# 将边索引转移到设备\n",
    "for edge_index in edge_indexs:\n",
    "    edge_index[0] = edge_index[0].to(device)\n",
    "    edge_index[1] = [tensor.to(device) for tensor in edge_index[1]]\n",
    "\n",
    "print(\"\\n=== Starting Pretraining ===\")\n",
    "\n",
    "bsne_model.classifier.requires_grad_(False)\n",
    "optimizer = torch.optim.AdamW(\n",
    "    filter(lambda p: p.requires_grad, bsne_model.parameters()),\n",
    "    lr=args['pretrain_lr'],\n",
    "    weight_decay=5e-5\n",
    ")\n",
    "pretrain_best_loss = float('inf')\n",
    "pretrain_no_improve = 0\n",
    "pretrain_early_stop = False\n",
    "\n",
    "for epoch in range(args['pretrain_epochs']):\n",
    "    if pretrain_early_stop:\n",
    "        break\n",
    "\n",
    "    bsne_model.train()\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 采样中心节点\n",
    "    batch_centers = rd.sample(range(feat_data.shape[0]), args['batch_size'])\n",
    "\n",
    "    # 计算TSNE损失\n",
    "#     tsne_loss = calculate_tsne_loss(\n",
    "#         model=bsne_model,\n",
    "#         feat_data=feat_data,\n",
    "#         dist_matrix=dist_matrix,\n",
    "#         batch_centers=batch_centers,\n",
    "#         sample_size=args['sample_size'],\n",
    "#         temperature=100,\n",
    "#         eps=1e-10,\n",
    "#         edge_indexs=edge_indexs,\n",
    "#         device=device\n",
    "#     )\n",
    "    \n",
    "    \n",
    "    sample_size=args['sample_size']\n",
    "    temperature=100\n",
    "    eps=1e-10\n",
    "    \n",
    "    \n",
    "    total_loss = 0.0\n",
    "    batch_size = len(batch_centers)\n",
    "\n",
    "    # 为每个中心节点构建Bp子图\n",
    "    bp_subgraphs = []\n",
    "    for xi in batch_centers:\n",
    "        # 根据距离矩阵计算采样概率\n",
    "        dist_row = dist_matrix[xi].cpu().numpy()\n",
    "        probs = np.exp(-dist_row)\n",
    "        probs[xi] = 0  # 排除自身\n",
    "        probs /= probs.sum()\n",
    "\n",
    "        # 采样邻居节点\n",
    "        available_nodes = len(dist_row) - 1\n",
    "        actual_sample_size = min(sample_size, available_nodes)\n",
    "        \n",
    "        if actual_sample_size > 0:\n",
    "            neighbors = np.random.choice(len(dist_row), size=actual_sample_size, p=probs, replace=False)\n",
    "            # 将中心节点和采样的邻居合并\n",
    "            subgraph_nodes = [xi] + neighbors.tolist()\n",
    "        else:\n",
    "            # 如果没有可用邻居，只使用中心节点\n",
    "            subgraph_nodes = [xi]\n",
    "            \n",
    "        bp_subgraphs.append(subgraph_nodes)\n",
    "\n",
    "    # 为每个中心节点构建Bu子图（均匀采样）\n",
    "    bu_subgraphs = []\n",
    "    for _ in range(batch_size):\n",
    "        # 随机采样节点构建Bu子图 (sample_size + 1个节点)\n",
    "        random_nodes = np.random.choice(feat_data.shape[0], size=sample_size + 1, replace=False)\n",
    "        bu_subgraphs.append(random_nodes.tolist())\n",
    "\n",
    "    # 计算Bp子图中所有节点的特征\n",
    "    bp_features_batch = []\n",
    "    for bp_nodes in bp_subgraphs:\n",
    "        bp_node_features = []\n",
    "        \n",
    "        # 为子图中的每个节点计算特征\n",
    "        for node_idx in bp_nodes:\n",
    "            # 构建该节点的一阶邻居子图\n",
    "            node_subgraph = create_node_subgraph(node_idx, feat_data, edge_indexs, device)\n",
    "            \n",
    "            # 获取节点特征\n",
    "            _, node_feature = bsne_model([node_subgraph])\n",
    "            \n",
    "            bp_node_features.append(node_feature.squeeze(0))\n",
    "            \n",
    "        # 将该Bp子图所有节点的特征收集起来\n",
    "        bp_features_batch.append(torch.stack(bp_node_features))  # [sample_size+1, d_model]\n",
    "\n",
    "    # 计算Bu子图中所有节点的特征\n",
    "    bu_features_batch = []\n",
    "    for bu_nodes in bu_subgraphs:\n",
    "        bu_node_features = []\n",
    "        \n",
    "        # 为子图中的每个节点计算特征\n",
    "        for node_idx in bu_nodes:\n",
    "            # 构建该节点的一阶邻居子图\n",
    "            node_subgraph = create_node_subgraph(node_idx, feat_data, edge_indexs, device)\n",
    "            \n",
    "            # 获取节点特征\n",
    "            _, node_feature = bsne_model([node_subgraph])\n",
    "            \n",
    "            bu_node_features.append(node_feature.squeeze(0))\n",
    "            \n",
    "        # 将该Bu子图所有节点的特征收集起来\n",
    "        bu_features_batch.append(torch.stack(bu_node_features))  # [sample_size+1, d_model]\n",
    "\n",
    "    # 针对每一对Bp和Bu子图计算loss\n",
    "    for i in range(batch_size):\n",
    "            # 获取中心节点索引和特征\n",
    "        center_idx = batch_centers[i]\n",
    "        center_feature = bp_features_batch[i][0]  # 中心节点是第一个\n",
    "\n",
    "        # 仅获取Bp子图中的其他节点（排除中心节点）\n",
    "        other_bp_indices = bp_subgraphs[i][1:]\n",
    "        other_bp_features = bp_features_batch[i][1:]\n",
    "\n",
    "        # 计算中心节点到其他Bp节点的原始距离\n",
    "        orig_dists_bp = dist_matrix[center_idx][other_bp_indices].cpu().numpy()\n",
    "\n",
    "        # 计算中心节点到其他Bp节点的特征距离\n",
    "        feat_dists_bp = torch.cdist(center_feature.unsqueeze(0), other_bp_features).squeeze(0)\n",
    "\n",
    "        # 计算P向量（基于原始距离）\n",
    "        P = (1.0 + orig_dists_bp ** 2) ** -1\n",
    "        P_sum = P.sum()\n",
    "        P = P / (P_sum + eps) if P_sum > 0 else torch.ones_like(P) / len(P)\n",
    "        P = torch.tensor(P, device=device)\n",
    "\n",
    "        # 计算Q向量（基于特征距离）\n",
    "        Q = (1.0 + feat_dists_bp ** 2 / temperature) ** -1\n",
    "        Q_sum = Q.sum()\n",
    "        Q = Q / (Q_sum + eps) if Q_sum > 0 else torch.ones_like(Q) / len(Q)\n",
    "\n",
    "        # 局部损失（KL散度）\n",
    "#         loss_local = (P * (torch.log(P + eps) - P * torch.log(Q + eps))).sum()\n",
    "        loss_local = (torch.log(P + eps) - torch.log(Q + eps)).sum() / len(bu_features_batch[i])\n",
    "    \n",
    "        # 计算k_Bp\n",
    "        sum_p_cond = P.sum()\n",
    "        N = dist_matrix.shape[0]\n",
    "        k_Bp = (sum_p_cond * N / len(other_bp_indices))\n",
    "\n",
    "        # 计算中心节点到Bu子图节点的特征距离\n",
    "        bu_features = bu_features_batch[i]\n",
    "        feat_dists_bu = torch.cdist(center_feature.unsqueeze(0), bu_features).squeeze(0)\n",
    "\n",
    "        # 计算指数距离和\n",
    "        sum_e_bu = torch.exp(-feat_dists_bu).sum()\n",
    "        sum_e_bp = torch.exp(-feat_dists_bp).sum()\n",
    "\n",
    "        # 全局损失\n",
    "        global_ratio = k_Bp * (sum_e_bu / (sum_e_bp + eps))\n",
    "        loss_global = torch.log(global_ratio.clamp(min=eps, max=1e10))\n",
    "\n",
    "        # 合并损失\n",
    "        total_loss += loss_local + loss_global\n",
    "#         print(f\"local_loss: {loss_local.item()},global_loss: {loss_global.item()}\")\n",
    "        \n",
    "#         bp_nodes = bp_subgraphs[i]\n",
    "#         bp_features = bp_features_batch[i]  # [bp_size, d_model]\n",
    "#         bu_features = bu_features_batch[i]  # [bu_size, d_model]\n",
    "        \n",
    "#         # 计算Bp子图中的距离矩阵（原始图距离）\n",
    "#         dist_bp = dist_matrix[bp_nodes][:, bp_nodes].cpu().numpy()\n",
    "        \n",
    "#         # 计算P矩阵（基于距离）\n",
    "#         P = (1.0 + dist_bp ** 2) ** -1\n",
    "#         np.fill_diagonal(P, 0)  # 对角线置零\n",
    "#         P = (P + P.T) / 2  # 对称化\n",
    "#         row_sums = P.sum(axis=1, keepdims=True)\n",
    "#         row_sums = np.where(row_sums == 0, eps, row_sums)\n",
    "#         P = P / row_sums\n",
    "#         P = torch.clamp(torch.tensor(P, device=device), min=eps)\n",
    "        \n",
    "#         # 计算Q矩阵（基于特征距离）\n",
    "#         pairwise_dist = torch.cdist(bp_features, bp_features, p=2)  # [bp_size, bp_size]\n",
    "#         Q = (1.0 + pairwise_dist ** 2 / temperature) ** -1\n",
    "#         Q.fill_diagonal_(0)\n",
    "#         Q = (Q + Q.permute(1, 0)) / 2\n",
    "#         Q_sum = Q.sum(dim=1, keepdim=True)\n",
    "#         Q_sum = torch.where(Q_sum == 0, torch.tensor(eps, device=device), Q_sum)\n",
    "#         Q = Q / Q_sum\n",
    "#         Q = torch.clamp(torch.tensor(Q, device=device), min=eps)\n",
    "        \n",
    "#         # 局部损失\n",
    "#         loss_local = (P * (torch.log(P) - torch.log(Q))).sum()\n",
    "        \n",
    "#         # 全局损失\n",
    "#         sum_p_sub = P.sum(dim=1)\n",
    "#         N = dist_matrix.shape[0]\n",
    "#         k_Bp = (sum_p_sub * N / len(bp_nodes)).mean()\n",
    "        \n",
    "#         # 计算Bp子图与Bu子图之间的特征距离\n",
    "#         d_bu = torch.exp(-torch.cdist(bp_features, bu_features, p=2))\n",
    "#         sum_e_bu = d_bu.sum()\n",
    "#         sum_e_bp = torch.exp(-pairwise_dist).sum()\n",
    "        \n",
    "#         global_ratio = k_Bp * (sum_e_bu / (sum_e_bp + eps))\n",
    "#         loss_global = torch.log(global_ratio.clamp(min=eps))\n",
    "        \n",
    "#         # 合并局部损失和全局损失\n",
    "#         total_loss += (loss_local + loss_global)\n",
    "#         print(f\"local_loss: {loss_local.item()},global_loss: {loss_global.item()}\")\n",
    "        \n",
    "    bsne_loss = total_loss / batch_size\n",
    "\n",
    "    \n",
    "    bsne_loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # 更新最佳损失和早停计数器\n",
    "    if bsne_loss.item() < pretrain_best_loss:\n",
    "        pretrain_best_loss = bsne_loss.item()\n",
    "        pretrain_no_improve = 0\n",
    "    else:\n",
    "        pretrain_no_improve += 1\n",
    "\n",
    "    if pretrain_no_improve >= args['pretrain_patience']:\n",
    "        print(f\"Pretrain early stopping at epoch {epoch}\")\n",
    "        pretrain_early_stop = True\n",
    "\n",
    "    writer.add_scalar('Pretrain/TSNE_Loss', bsne_loss.item(), epoch)\n",
    "\n",
    "    if epoch % 5 == 0:\n",
    "        print(f'Pretrain Epoch: {epoch:03d}, BSNE Loss: {bsne_loss.item():.4f}')\n",
    "\n",
    "print(\"\\n=== Starting Fine-tuning ===\")\n",
    "bsne_model.classifier.requires_grad_(True)\n",
    "optimizer = torch.optim.AdamW(\n",
    "    bsne_model.parameters(),\n",
    "    lr=args['finetune_lr'],\n",
    "    weight_decay=5e-5\n",
    ")\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
    "    optimizer, mode='max', factor=0.5, patience=10, verbose=True\n",
    ")\n",
    "\n",
    "best_val_auc = 0.0\n",
    "best_model_state = None\n",
    "train_pos, train_neg = pos_neg_split(idx_train, y_train)\n",
    "\n",
    "no_improve_epochs = 0\n",
    "early_stop = False\n",
    "\n",
    "for epoch in range(args['num_epochs']):\n",
    "    if early_stop:\n",
    "        break\n",
    "\n",
    "    bsne_model.train()\n",
    "    total_loss = 0.0\n",
    "\n",
    "    # 采样中心节点\n",
    "    batch_centers = rd.sample(train_pos + train_neg, args['batch_size'])\n",
    "    subgraph_data = []\n",
    "\n",
    "    for xi in batch_centers:\n",
    "        subgraph = create_node_subgraph(xi, feat_data, edge_indexs, device)\n",
    "#         print(len(subgraph['features']))\n",
    "        subgraph_data.append(subgraph)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 前向传播\n",
    "    center_logits, _ = bsne_model(subgraph_data)  # [B, 2]\n",
    "\n",
    "    # 提取中心节点标签\n",
    "    labels_center = torch.tensor([labels[xi] for xi in batch_centers]).to(device).long()\n",
    "\n",
    "    # 计算分类损失\n",
    "    cls_loss = F.nll_loss(center_logits, labels_center)\n",
    "\n",
    "    cls_loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    total_loss += cls_loss.item()\n",
    "\n",
    "    avg_loss = total_loss / args['batch_size']\n",
    "    writer.add_scalar('FineTune/Train_Loss', avg_loss, epoch)\n",
    "\n",
    "    if epoch % 5 == 0:\n",
    "        val_auc, val_ap, val_f1, val_g_mean = test(idx_val, y_val, bsne_model, feat_data, edge_indexs, device)\n",
    "\n",
    "        writer.add_scalar('Validation/AUC', val_auc, epoch)\n",
    "        writer.add_scalar('Validation/F1', val_f1, epoch)\n",
    "        writer.add_scalar('Validation/GMean', val_g_mean, epoch)\n",
    "\n",
    "        print(f'Epoch: {epoch:03d} | Loss: {avg_loss:.4f} | Val AUC: {val_auc:.4f} | Val F1: {val_f1:.4f}')\n",
    "\n",
    "        scheduler.step(val_auc)\n",
    "\n",
    "        if val_auc > best_val_auc:\n",
    "            best_val_auc = val_auc\n",
    "            no_improve_epochs = 0\n",
    "            best_model_state = copy.deepcopy(bsne_model.state_dict())\n",
    "        else:\n",
    "            no_improve_epochs += 1\n",
    "\n",
    "        if no_improve_epochs >= args['patience']:\n",
    "            print(f\"Early stopping at epoch {epoch}\")\n",
    "            early_stop = True\n",
    "\n",
    "# 加载最佳模型\n",
    "bsne_model.load_state_dict(best_model_state)\n",
    "\n",
    "# 最终测试\n",
    "test_auc, test_ap, test_f1, test_g_mean = test(idx_test, y_test, bsne_model, feat_data, edge_indexs, device)\n",
    "print(f'\\n=== Final Test Results ===')\n",
    "print(f'Test AUC: {test_auc:.4f} | Test AP: {test_ap:.4f} | Test F1: {test_f1:.4f} | G-mean: {test_g_mean:.4f}')\n",
    "writer.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a41a50c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n",
      "loading data...\n",
      "\n",
      "=== Starting Fine-tuning ===\n",
      "Epoch: 000 | Loss: 0.1717 | Val AUC: 0.8141 | Val F1: 0.5190\n",
      "Epoch: 005 | Loss: 0.0117 | Val AUC: 0.8416 | Val F1: 0.4751\n",
      "Epoch: 010 | Loss: 0.0046 | Val AUC: 0.8514 | Val F1: 0.4751\n",
      "Epoch: 015 | Loss: 0.0093 | Val AUC: 0.8583 | Val F1: 0.4751\n",
      "Epoch: 020 | Loss: 0.0050 | Val AUC: 0.8623 | Val F1: 0.4751\n",
      "Epoch: 025 | Loss: 0.0070 | Val AUC: 0.8627 | Val F1: 0.4751\n",
      "Epoch: 030 | Loss: 0.0061 | Val AUC: 0.8639 | Val F1: 0.4751\n",
      "Epoch: 035 | Loss: 0.0084 | Val AUC: 0.8706 | Val F1: 0.5311\n",
      "Epoch: 040 | Loss: 0.0098 | Val AUC: 0.8813 | Val F1: 0.5392\n",
      "Epoch: 045 | Loss: 0.0073 | Val AUC: 0.8935 | Val F1: 0.6732\n",
      "Epoch: 050 | Loss: 0.0093 | Val AUC: 0.9035 | Val F1: 0.7455\n",
      "Epoch: 055 | Loss: 0.0327 | Val AUC: 0.9066 | Val F1: 0.7146\n",
      "Epoch: 060 | Loss: 0.0468 | Val AUC: 0.9039 | Val F1: 0.6165\n",
      "Epoch: 065 | Loss: 0.0201 | Val AUC: 0.9038 | Val F1: 0.5636\n",
      "Epoch: 070 | Loss: 0.0429 | Val AUC: 0.9029 | Val F1: 0.6462\n",
      "Epoch: 075 | Loss: 0.0294 | Val AUC: 0.9017 | Val F1: 0.6807\n",
      "Epoch: 080 | Loss: 0.0277 | Val AUC: 0.9041 | Val F1: 0.6290\n",
      "Epoch: 085 | Loss: 0.1157 | Val AUC: 0.9049 | Val F1: 0.5642\n",
      "Epoch: 090 | Loss: 0.0032 | Val AUC: 0.9072 | Val F1: 0.7372\n",
      "Epoch: 095 | Loss: 0.0210 | Val AUC: 0.9111 | Val F1: 0.7743\n",
      "\n",
      "=== Final Test Results ===\n",
      "Test AUC: 0.9008 | Test AP: 0.4724 | Test F1: 0.7603 | G-mean: 0.7881\n"
     ]
    }
   ],
   "source": [
    "# 只进行分类loss训练\n",
    "\n",
    "# 参数设置\n",
    "args = {\n",
    "    \"dataset\": \"amazon\",\n",
    "    \"batch_size\": 8,\n",
    "    \"sample_size\": 20,\n",
    "    \"weight_decay\": 0.00005,\n",
    "    \"emb_size\": 32,\n",
    "    \"pretrain_epochs\": 50,\n",
    "    \"pretrain_lr\": 0.0001,\n",
    "    \"finetune_lr\": 0.0005,\n",
    "    \"num_epochs\": 100,\n",
    "    \"pretrain_patience\": 20,\n",
    "    \"patience\": 30,\n",
    "    \"tsne_weight\": 0.3,\n",
    "    \"weight\": 0.6,\n",
    "    \"layers\": 7,\n",
    "    \"test_size\": 0.6,\n",
    "    \"val_size\": 0.5,\n",
    "    \"layers_tree\": 7,\n",
    "    \"seed\": 76,\n",
    "    \"num_heads\": 2,\n",
    "    \"drop_rate\": 0.5\n",
    "}\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)\n",
    "\n",
    "timestamp = time.strftime(\"%Y%m%d-%H%M%S\")\n",
    "writer = SummaryWriter(f'runs/{args[\"dataset\"]}_{timestamp}')\n",
    "\n",
    "print('loading data...')\n",
    "prefix = \"../../data/\"\n",
    "\n",
    "edge_indexs, feat_data, labels = load_data(args['dataset'], args['layers_tree'], prefix)\n",
    "\n",
    "np.random.seed(args['seed'])\n",
    "rd.seed(args['seed'])\n",
    "\n",
    "if args['dataset'] == 'yelp':\n",
    "    index = list(range(len(labels)))\n",
    "    idx_train_val, idx_test, y_train_val, y_test = train_test_split(index, labels, stratify=labels,\n",
    "                                                                    test_size=args['test_size'], random_state=2,\n",
    "                                                                    shuffle=True)\n",
    "    idx_train, idx_val, y_train, y_val = train_test_split(idx_train_val, y_train_val,\n",
    "                                                          stratify=y_train_val, test_size=args['val_size'],\n",
    "                                                          random_state=2, shuffle=True)\n",
    "    dist_path = os.path.join(prefix, \"YelpChi_shortest_distance.pkl\")\n",
    "elif args['dataset'] == 'amazon':\n",
    "    index = list(range(3305, len(labels)))\n",
    "    idx_train_val, idx_test, y_train_val, y_test = train_test_split(index, labels[3305:],\n",
    "                                                                    stratify=labels[3305:],\n",
    "                                                                    test_size=args['test_size'],\n",
    "                                                                    random_state=2, shuffle=True)\n",
    "    idx_train, idx_val, y_train, y_val = train_test_split(idx_train_val, y_train_val,\n",
    "                                                          stratify=y_train_val, test_size=args['val_size'],\n",
    "                                                          random_state=2, shuffle=True)\n",
    "    dist_path = os.path.join(prefix, \"Amazon_shortest_distance.pkl\")\n",
    "\n",
    "with open(dist_path, 'rb') as f:\n",
    "    dist_data = pickle.load(f)\n",
    "    dist_matrix = torch.tensor(dist_data['dist_matrix']).to(device)\n",
    "\n",
    "# 准备特征数据\n",
    "feat_data = torch.tensor(feat_data).float()\n",
    "# 特征归一化\n",
    "scaler = MinMaxScaler()\n",
    "feat_data = torch.tensor(scaler.fit_transform(feat_data)).float().to(device)\n",
    "\n",
    "# 初始化模型\n",
    "bsne_model = BSNE_Transformer(\n",
    "    in_feat=feat_data.shape[1],\n",
    "    out_feat=2,\n",
    "    relation_nums=len(edge_indexs),\n",
    "    d_model=64,\n",
    "    nhead=args['num_heads'],\n",
    "    num_layers=3,\n",
    "    dim_feedforward=256,\n",
    "    drop_rate=args['drop_rate']\n",
    ").to(device)\n",
    "\n",
    "# 将边索引转移到设备\n",
    "for edge_index in edge_indexs:\n",
    "    edge_index[0] = edge_index[0].to(device)\n",
    "    edge_index[1] = [tensor.to(device) for tensor in edge_index[1]]\n",
    "\n",
    "print(\"\\n=== Starting Fine-tuning ===\")\n",
    "bsne_model.classifier.requires_grad_(True)\n",
    "optimizer = torch.optim.AdamW(\n",
    "    bsne_model.parameters(),\n",
    "    lr=args['finetune_lr'],\n",
    "    weight_decay=5e-5\n",
    ")\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
    "    optimizer, mode='max', factor=0.5, patience=10, verbose=True\n",
    ")\n",
    "\n",
    "best_val_auc = 0.0\n",
    "best_model_state = None\n",
    "train_pos, train_neg = pos_neg_split(idx_train, y_train)\n",
    "\n",
    "no_improve_epochs = 0\n",
    "early_stop = False\n",
    "\n",
    "for epoch in range(args['num_epochs']):\n",
    "    if early_stop:\n",
    "        break\n",
    "\n",
    "    bsne_model.train()\n",
    "    total_loss = 0.0\n",
    "\n",
    "    # 采样中心节点\n",
    "    batch_centers = rd.sample(train_pos + train_neg, args['batch_size'])\n",
    "    subgraph_data = []\n",
    "\n",
    "    for xi in batch_centers:\n",
    "        subgraph = create_node_subgraph(xi, feat_data, edge_indexs, device)\n",
    "#         print(len(subgraph['features']))\n",
    "        subgraph_data.append(subgraph)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 前向传播\n",
    "    center_logits, _ = bsne_model(subgraph_data)  # [B, 2]\n",
    "\n",
    "    # 提取中心节点标签\n",
    "    labels_center = torch.tensor([labels[xi] for xi in batch_centers]).to(device).long()\n",
    "\n",
    "    # 计算分类损失\n",
    "    cls_loss = F.nll_loss(center_logits, labels_center)\n",
    "\n",
    "    cls_loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    total_loss += cls_loss.item()\n",
    "\n",
    "    avg_loss = total_loss / args['batch_size']\n",
    "    writer.add_scalar('FineTune/Train_Loss', avg_loss, epoch)\n",
    "\n",
    "    if epoch % 5 == 0:\n",
    "        val_auc, val_ap, val_f1, val_g_mean = test(idx_val, y_val, bsne_model, feat_data, edge_indexs, device)\n",
    "\n",
    "        writer.add_scalar('Validation/AUC', val_auc, epoch)\n",
    "        writer.add_scalar('Validation/F1', val_f1, epoch)\n",
    "        writer.add_scalar('Validation/GMean', val_g_mean, epoch)\n",
    "\n",
    "        print(f'Epoch: {epoch:03d} | Loss: {avg_loss:.4f} | Val AUC: {val_auc:.4f} | Val F1: {val_f1:.4f}')\n",
    "\n",
    "        scheduler.step(val_auc)\n",
    "\n",
    "        if val_auc > best_val_auc:\n",
    "            best_val_auc = val_auc\n",
    "            no_improve_epochs = 0\n",
    "            best_model_state = copy.deepcopy(bsne_model.state_dict())\n",
    "        else:\n",
    "            no_improve_epochs += 1\n",
    "\n",
    "        if no_improve_epochs >= args['patience']:\n",
    "            print(f\"Early stopping at epoch {epoch}\")\n",
    "            early_stop = True\n",
    "\n",
    "# 加载最佳模型\n",
    "bsne_model.load_state_dict(best_model_state)\n",
    "\n",
    "# 最终测试\n",
    "test_auc, test_ap, test_f1, test_g_mean = test(idx_test, y_test, bsne_model, feat_data, edge_indexs, device)\n",
    "print(f'\\n=== Final Test Results ===')\n",
    "print(f'Test AUC: {test_auc:.4f} | Test AP: {test_ap:.4f} | Test F1: {test_f1:.4f} | G-mean: {test_g_mean:.4f}')\n",
    "writer.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c327956a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10810, 9559, 5472, 6150, 3862, 10195, 6961, 6622]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f646f914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(subgraph_data[1]['features'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2202df0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.0000, 0.1633, 0.0000,  ..., 1.0000, 0.1654, 1.0000],\n",
      "        [0.0000, 0.3265, 0.1429,  ..., 1.0000, 0.1024, 1.0000],\n",
      "        [0.0000, 0.8367, 0.0000,  ..., 1.0000, 0.1417, 1.0000],\n",
      "        ...,\n",
      "        [0.0104, 0.3061, 0.0000,  ..., 0.0000, 0.3150, 1.0000],\n",
      "        [0.0000, 0.3469, 0.0000,  ..., 1.0000, 0.1496, 1.0000],\n",
      "        [0.0000, 0.1837, 0.0000,  ..., 1.0000, 0.2598, 1.0000]],\n",
      "       device='cuda:0')\n",
      "tensor([[0.0021, 0.3061, 0.0000,  ..., 0.0000, 0.1220, 1.0000],\n",
      "        [0.0041, 0.1633, 0.0000,  ..., 0.0000, 0.1654, 1.0000],\n",
      "        [0.0021, 0.1429, 0.0000,  ..., 0.0000, 0.1417, 1.0000],\n",
      "        ...,\n",
      "        [0.0104, 0.5102, 0.0000,  ..., 0.0000, 0.2388, 1.0000],\n",
      "        [0.0851, 0.1633, 0.0000,  ..., 0.0000, 0.2430, 1.0000],\n",
      "        [0.0021, 0.1837, 0.0000,  ..., 0.0000, 0.2992, 1.0000]],\n",
      "       device='cuda:0')\n",
      "400\n",
      "400\n",
      "400\n",
      "400\n",
      "400\n",
      "180\n",
      "400\n",
      "400\n"
     ]
    }
   ],
   "source": [
    "print(subgraph_data[0]['features'])\n",
    "print(subgraph_data[1]['features'])\n",
    "print(len(subgraph_data[0]['features']))\n",
    "print(len(subgraph_data[1]['features']))\n",
    "print(len(subgraph_data[2]['features']))\n",
    "print(len(subgraph_data[3]['features']))\n",
    "print(len(subgraph_data[4]['features']))\n",
    "print(len(subgraph_data[5]['features']))\n",
    "print(len(subgraph_data[6]['features']))\n",
    "print(len(subgraph_data[7]['features']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0b342553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.1948, -1.7318],\n",
       "        [-0.1294, -2.1089],\n",
       "        [-0.5026, -0.9287],\n",
       "        [-1.4341, -0.2722],\n",
       "        [-0.2592, -1.4771],\n",
       "        [    nan,     nan],\n",
       "        [-0.4153, -1.0793],\n",
       "        [-0.1773, -1.8175]], device='cuda:0', grad_fn=<LogSoftmaxBackward0>)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "center_logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "87f51fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[    nan,     nan],\n",
       "        [-0.5853, -0.8141],\n",
       "        [    nan,     nan],\n",
       "        [-0.8879, -0.5302],\n",
       "        [-1.3848, -0.2882],\n",
       "        [    nan,     nan],\n",
       "        [    nan,     nan],\n",
       "        [-0.3493, -1.2213]], device='cuda:0', grad_fn=<LogSoftmaxBackward0>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "center_logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "21be5bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[    nan,     nan],\n",
       "        [-1.5708, -0.2330],\n",
       "        [    nan,     nan],\n",
       "        [-0.1267, -2.1284],\n",
       "        [-0.2299, -1.5828],\n",
       "        [    nan,     nan],\n",
       "        [    nan,     nan],\n",
       "        [-0.8184, -0.5819]], device='cuda:0', grad_fn=<LogSoftmaxBackward0>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "center_logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18ccbbc7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa97b53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ee4dff7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a243c33f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f327b9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46c14bca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f236b0be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "07156810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "center_features: tensor([[-0.1000,  0.2624, -0.9874, -0.4235,  0.4917,  0.5208, -1.4995,  0.9382,\n",
      "         -0.3624, -1.8993,  0.3318,  0.1482,  0.4476,  0.6003,  0.2794, -0.6996,\n",
      "          0.1620, -2.6017,  0.0575,  0.3301, -0.1448,  0.4512, -1.6678,  0.2005,\n",
      "         -1.9614, -0.9502,  1.1130,  0.7534, -0.1312,  1.9596, -0.5913, -0.0292,\n",
      "         -0.4757, -0.1434, -0.0839,  1.2567, -0.7540, -2.0317,  0.2082,  1.8624,\n",
      "          0.3865,  0.9913,  0.4698,  1.1662,  2.7105,  0.7472,  0.5558, -0.1767,\n",
      "         -2.4726,  1.1501, -0.3890, -0.2059, -0.4173, -0.4386,  1.2705, -0.2981,\n",
      "         -0.8374,  0.2403,  1.0381, -0.1702, -0.1704, -0.6687,  0.6125,  0.0689,\n",
      "         -0.0624, -0.1729,  1.2102,  0.9607,  0.1668, -0.1098, -1.5206, -0.7572,\n",
      "         -1.7948,  0.3795, -0.1449,  0.4211, -2.0289, -0.5047, -2.4189,  0.4584,\n",
      "          1.6580, -1.1791,  1.3159,  0.2964, -0.7694,  1.6905, -0.5812, -0.4399,\n",
      "         -1.7579, -0.9615, -0.5697, -0.5267, -0.1838,  1.2363, -0.1626,  2.6182,\n",
      "          0.4011, -1.0621,  0.6232,  0.1784, -0.8742, -0.9148, -1.9447,  0.4040,\n",
      "          0.5768,  0.1398,  0.3749, -0.7191,  0.2748,  0.7342,  0.6825,  0.6838,\n",
      "         -0.2416,  2.6937, -0.1766,  1.6129, -0.1122,  0.3198, -0.1672,  0.0399,\n",
      "         -0.4999, -0.1308, -0.3054,  0.4084,  0.1195,  1.0918, -0.1132,  0.1371,\n",
      "          0.3669, -0.1153,  1.0965,  0.0861, -1.0281, -0.0923, -0.8988,  2.1353,\n",
      "          0.1020,  0.6347,  0.4795, -0.1874,  0.2545,  0.2447, -0.0435,  1.4811,\n",
      "         -0.0263, -1.5760, -1.4180,  0.1076, -0.0280, -0.3646, -0.7478, -1.5506,\n",
      "         -1.7233,  0.6056,  2.8975, -0.5432, -1.1402, -0.3143,  0.8129,  1.2580,\n",
      "          0.1493, -0.1891, -0.8748,  0.4547, -0.1308,  1.7483,  1.6097,  0.2771,\n",
      "         -0.6030,  1.4256, -0.5558,  0.8777,  0.4853,  0.5324,  1.2874, -1.8247,\n",
      "         -0.4934, -2.2045, -1.3373,  0.6664, -1.2259,  0.0618,  0.0787, -0.1318,\n",
      "         -0.5788, -0.5897,  0.5620,  1.7989, -0.4249, -0.6563, -0.5896, -0.3703]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-4.2985e-01,  1.0334e+00, -4.1255e-01,  9.8638e-03,  7.6137e-02,\n",
      "          1.5274e+00, -1.7448e+00,  8.8860e-01,  4.8875e-01, -5.8961e-01,\n",
      "          2.8943e-01,  1.2349e+00,  1.3814e+00, -1.4442e-01,  6.3370e-01,\n",
      "         -1.4480e+00,  5.0568e-01,  1.0469e+00,  1.2624e-02, -5.3674e-01,\n",
      "          1.2170e-01, -1.0854e+00,  1.7951e+00,  2.1306e+00, -1.9770e+00,\n",
      "         -2.4167e-01, -1.4748e+00, -1.3693e-01, -3.2362e-02,  3.5919e-01,\n",
      "          1.0868e-01, -2.1138e-03,  1.4090e-01,  1.3515e-01, -4.6389e-01,\n",
      "          5.8265e-01,  1.1099e+00, -4.6696e-01,  2.6169e-01, -1.1256e+00,\n",
      "         -2.2595e+00,  7.6254e-01,  3.1429e-01, -1.8844e-01,  2.7655e+00,\n",
      "          1.2503e+00, -5.1906e-02, -2.9844e-01,  4.5083e-01,  1.6787e+00,\n",
      "         -3.8414e-01, -2.3513e-01,  4.3933e-01, -8.5071e-02, -2.4860e-01,\n",
      "         -1.9336e+00, -1.0760e+00, -9.9183e-01, -1.8243e+00,  6.2744e-02,\n",
      "         -1.1110e+00,  1.1054e-01, -6.7020e-01, -3.8689e-02,  4.1074e-01,\n",
      "         -9.7037e-02,  1.2673e-01,  5.6111e-01,  1.2779e+00, -3.0946e-01,\n",
      "         -3.5028e-01, -8.8454e-01,  9.2958e-02,  1.0199e+00, -1.3127e+00,\n",
      "          4.6137e-01,  6.0373e-02, -3.7446e-01, -1.0349e+00,  3.6089e-01,\n",
      "          8.5061e-01, -3.7276e-01, -4.2216e-02,  2.4341e-01, -6.0207e-01,\n",
      "          3.4320e-01, -8.5327e-01, -1.6945e+00, -4.2472e-01,  3.3739e-01,\n",
      "         -3.9135e-01, -1.2267e+00,  9.0599e-01, -1.3040e+00, -2.5322e-01,\n",
      "          5.3817e-01,  3.5991e-01, -3.7407e-01, -1.5784e-01,  3.0109e-01,\n",
      "         -7.7133e-01,  1.3792e+00, -1.9142e+00, -4.9799e-01, -1.4771e+00,\n",
      "         -9.5036e-02,  3.6370e-01, -1.5552e+00,  3.0005e+00,  2.0146e-01,\n",
      "          4.6011e-01,  1.0843e+00,  1.1267e+00,  2.7744e-01,  1.5060e+00,\n",
      "          4.0363e-01,  5.9974e-01,  1.4714e+00, -3.1639e+00,  6.7469e-01,\n",
      "         -1.5251e-01, -1.2638e-01, -3.8691e-01,  1.3847e+00, -2.0982e-01,\n",
      "          1.4497e+00, -1.8316e+00,  6.0685e-01,  1.5677e-01,  1.4142e-01,\n",
      "         -1.8337e+00,  2.4357e-01, -4.7795e-01, -1.6605e+00, -1.7391e-01,\n",
      "         -1.9604e-01,  1.9173e-01,  1.7615e+00,  4.5020e-01, -1.7714e+00,\n",
      "         -1.3630e+00,  3.0054e-01,  1.6098e+00, -4.0583e-02, -1.6608e-02,\n",
      "          1.6206e+00, -7.5556e-01,  2.8084e-01,  1.4593e+00, -6.9063e-01,\n",
      "         -1.0059e+00,  5.6910e-01, -1.6141e-01,  6.5540e-01,  4.3579e-01,\n",
      "          3.7075e-02,  1.5076e-02,  1.1464e+00,  6.4679e-02, -6.6983e-01,\n",
      "          1.0717e+00, -1.7012e-01, -2.4285e+00, -1.4967e-01, -4.0846e-02,\n",
      "          6.8680e-01,  8.7414e-01,  2.2336e-01, -6.5407e-01,  1.3915e+00,\n",
      "          8.7469e-01,  1.8460e+00,  2.3495e+00, -1.2743e+00,  4.8586e-01,\n",
      "         -5.1808e-01, -3.0403e+00,  1.4091e-01, -8.6396e-01, -6.2318e-01,\n",
      "          5.9051e-02, -2.3195e-01, -9.6839e-02, -6.1354e-01, -1.2805e-01,\n",
      "         -2.6107e-01,  6.0653e-01, -9.4544e-01,  2.9616e-01, -3.2550e-01,\n",
      "         -2.1923e-01,  1.3557e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 0.7770, -0.0058,  1.0718, -0.8569, -0.6483, -0.5075,  0.7997,  1.4412,\n",
      "         -0.8765, -2.1752,  0.1999,  0.0509, -0.7947, -1.3220, -0.2608, -0.3431,\n",
      "          2.9436,  0.0736,  0.7947,  0.9612, -0.6409,  2.1491, -0.4699, -0.4064,\n",
      "          0.9914,  0.2380, -1.5612,  0.4120, -0.2692, -1.4786, -0.2691,  0.2624,\n",
      "         -0.2350,  0.7895,  1.7762,  1.1793, -1.7145,  0.1737, -0.4431, -0.6760,\n",
      "          1.5101, -0.3683,  0.2860, -0.4133,  0.1213, -0.0797,  0.0774, -0.6298,\n",
      "          0.0335,  1.4807, -0.3154,  1.6073, -0.4034,  1.2999,  0.2687,  0.6170,\n",
      "         -2.2640, -0.5286, -0.9301, -0.3614, -0.3486, -1.6353, -0.0182, -0.1364,\n",
      "         -0.4400,  0.4603, -0.2615, -0.5202,  0.4038, -1.8225,  0.7450, -1.1993,\n",
      "          1.7174,  1.0383, -0.4314,  0.3403,  0.3317, -0.1040, -0.2255, -0.0273,\n",
      "          2.5396,  0.5133,  0.5052,  0.0943,  0.8195,  1.8370,  1.2010, -1.6330,\n",
      "          1.7746, -1.0568,  0.2151, -1.2644, -0.0712, -0.7268,  0.6457, -0.0294,\n",
      "         -0.9826,  0.0885, -0.2582, -0.2867,  0.0292, -0.9752, -1.8778, -0.1137,\n",
      "          0.0111, -2.5482,  1.0426, -1.5572,  1.8317,  0.3837, -0.0786,  0.5447,\n",
      "          0.4880,  0.3563, -0.0106, -0.1589, -2.8049,  1.0439,  0.2399,  0.6694,\n",
      "         -0.6564, -0.0894,  0.3363,  0.2135,  0.0324, -0.6338,  0.8668, -0.5145,\n",
      "          0.4103,  0.1378,  1.1945,  1.3306, -1.1790, -0.2453, -0.3109, -0.1330,\n",
      "          3.1838,  2.0346, -0.1173,  0.2183, -0.5213, -0.6142, -0.7423,  0.8342,\n",
      "         -0.4663, -0.6199, -0.5962, -1.4886,  0.3690, -0.4176, -1.4867, -0.2453,\n",
      "         -0.2615, -0.7222,  0.2061,  0.5749,  1.0325, -1.5863, -0.2378, -0.3146,\n",
      "          0.6757,  0.3113,  0.0916,  0.4649, -0.1368,  0.2870,  1.1335,  0.2596,\n",
      "         -0.5224,  1.9664,  1.7558, -0.0964, -1.5806,  0.6148, -1.2863, -0.3583,\n",
      "         -0.4151, -0.8830, -0.7816,  0.1761,  0.3381, -0.5780, -0.6859,  0.7375,\n",
      "         -1.0655, -0.2575,  2.5435, -0.5335,  0.1329,  0.2615,  0.7714, -2.5612]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -3.2055e-01, -1.0667e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -0.6799, -2.4505,  0.7088,  1.1883, -0.1978, -0.0398, -0.3640, -1.0472,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -5.4161e-02, -1.5372e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 7.8305e-02,  7.8353e-02, -1.8457e-01, -1.8090e-01, -1.4271e-02,\n",
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      "          1.9093e+00,  5.3628e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -3.8494e-01, -1.2082e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -2.4889e-01,  9.4862e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "center_features: tensor([[ 1.6374e+00,  4.4418e-01, -6.3004e-01,  1.0980e-01,  3.1433e-01,\n",
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      "         -2.0175e+00, -7.5221e-01, -9.8826e-01, -3.2817e-01, -1.5860e-01,\n",
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      "         -1.0880e-01, -2.0851e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          5.7245e-01, -5.0745e-01,  1.3994e-01, -4.3336e-02, -9.9711e-01,\n",
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      "         -5.8377e-01,  1.4112e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 2.7912e-01,  2.5179e-01, -6.2666e-01,  1.1171e+00,  3.9896e-01,\n",
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      "          5.7086e-02,  1.8366e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-0.1535,  1.2821, -1.1849, -0.1907, -0.2215, -0.5480, -0.4597,  0.2927,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-6.2594e-01,  4.7122e-02,  1.5101e-01,  5.6519e-01,  2.2618e+00,\n",
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      "         -1.3226e+00, -2.5381e-02, -1.7172e-02,  6.1229e-01,  1.5283e+00,\n",
      "          1.0831e+00, -1.3802e-02,  1.3417e-02,  7.1325e-01, -5.5124e-01,\n",
      "          1.6594e+00, -3.0539e-02, -4.6067e-01,  1.1151e+00, -1.7861e+00,\n",
      "          5.3807e-01, -1.5611e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -1.2507e+00, -5.5395e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -6.2095e-01,  5.2575e-01, -1.3065e+00, -1.3843e+00, -1.8364e+00,\n",
      "         -3.3249e-01, -5.7761e-01,  8.6589e-02,  2.6039e-01, -9.8886e-02,\n",
      "         -9.2430e-01,  7.5667e-01,  2.0095e+00,  3.7241e-01,  9.3391e-01,\n",
      "         -4.0407e-01, -3.2973e-02]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "center_features: tensor([[ 3.4793e-01, -7.0494e-02,  1.0919e+00,  6.4412e-01,  7.0366e-01,\n",
      "         -1.2859e+00, -7.9748e-02, -5.1436e-02,  1.6822e-01, -8.3558e-01,\n",
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      "          1.3805e+00, -1.1697e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-2.0724e-01,  6.1014e-01,  2.8304e-02,  9.0338e-01,  6.0039e-01,\n",
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      "         -1.4359e+00,  3.5976e-02]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 0.1776,  3.6447, -0.3408, -0.1675, -1.1534, -0.9794,  0.4219, -2.3257,\n",
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      "          1.1395,  0.8102,  1.0346, -2.1931,  0.0817, -0.5705, -0.7880,  0.6603]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-1.3117e+00,  7.9749e-01,  1.7512e+00, -5.6232e-01,  2.7718e+00,\n",
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      "         -1.2691e+00, -1.2313e+00, -1.8048e+00,  2.3178e+00,  2.8604e+00,\n",
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      "         -9.0320e-01,  7.5573e-01, -2.5707e-01,  3.8470e-01,  5.7408e-01,\n",
      "          6.6129e-01,  1.1072e+00, -3.0048e-01, -6.8263e-01, -3.0846e-01,\n",
      "         -6.6017e-01, -2.1731e+00, -3.8722e-01,  9.9151e-01,  1.1924e+00,\n",
      "         -8.2238e-01, -2.2117e-01,  5.9547e-01,  3.2664e-01, -2.5308e-01,\n",
      "          1.0471e-01,  2.8765e+00, -2.9698e-01, -4.9233e-01, -5.7123e-01,\n",
      "          5.2567e-01,  4.0777e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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     "name": "stdout",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          9.4792e-01,  3.5327e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -5.1452e-01, -1.8320e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -7.1116e-01,  5.2161e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
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     "name": "stdout",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          1.2754,  0.3073, -0.7144,  0.1134, -1.9304, -1.7247, -0.3548,  0.8159]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -2.9075e+00, -1.2479e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -3.1227e-01, -1.7865e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -4.6284e-01, -1.5776e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -9.8780e-02,  4.0106e-01,  6.6287e-01,  5.6566e-01, -1.0454e+00,\n",
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      "         -1.3319e+00,  3.0183e+00,  1.5840e+00,  4.9449e-02, -1.8307e+00,\n",
      "          5.4782e-01, -1.9100e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    },
    {
     "name": "stdout",
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     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -1.5045, -0.6684,  0.5828, -0.9634, -2.0646,  1.2173,  0.0658, -1.8215,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          3.1139e-01,  8.4253e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          0.8183,  0.6089,  0.7436,  2.6386,  0.3585, -1.1735,  1.3006, -0.2291]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          2.5938e-01,  4.0500e-01,  7.7043e-01, -2.7173e-01,  1.3135e+00,\n",
      "         -1.1956e+00, -5.4170e-01, -7.4116e-01, -2.1881e-01, -2.6326e-01,\n",
      "         -1.2897e+00,  4.4003e-01,  3.7914e-01, -3.0560e-01, -9.3005e-01,\n",
      "         -4.1937e-01, -6.1549e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "center_features: tensor([[-2.0165e+00, -9.7288e-01, -4.4713e-02,  5.6275e-01,  2.3533e-01,\n",
      "         -8.5766e-01,  9.6659e-02, -7.7289e-01, -9.9394e-01, -5.1099e-01,\n",
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      "         -4.5909e-01,  9.2919e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 3.9206e-01,  4.4987e-01,  1.1842e+00, -9.9145e-01, -6.8488e-01,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-3.9381e-01, -3.0793e-01, -1.7636e+00,  3.0071e-01,  9.7110e-01,\n",
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     "name": "stdout",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 2.2873e+00,  1.4855e-01, -1.4170e-01,  2.6156e-01, -2.4392e-01,\n",
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      "         -5.5123e-01, -1.0557e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 0.2308, -0.7896, -0.3367, -0.2948,  3.0589,  1.2788,  1.2326, -1.0949,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -1.9083e-01,  3.3760e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          7.6762e-01, -9.4066e-02]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -2.0755e+00,  4.4418e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "center_features: tensor([[ 1.5010e-01,  7.0010e-01,  6.0107e-02,  2.9310e-01,  7.4352e-02,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -1.2296e+00, -2.8615e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          6.1059e-01, -5.1613e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "center_features: tensor([[-6.4059e-01,  2.4442e-01, -2.6814e-01,  2.5143e-01,  1.5912e+00,\n",
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      "         -2.4613e-01, -4.4669e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -1.1190e+00,  1.2285e-02]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
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     "name": "stdout",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          2.5985e-01,  2.7368e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-2.2967e-01, -1.0864e-01,  8.1187e-01,  1.1284e+00,  4.9509e-01,\n",
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      "         -2.5966e-01, -4.0925e-01,  5.2311e-01, -6.0461e-01,  1.3741e+00,\n",
      "         -7.0260e-01,  1.8015e+00,  1.3698e+00, -3.6574e-01,  1.4157e+00,\n",
      "         -7.5023e-01,  8.3569e-01, -4.0737e-01,  2.6638e-02, -6.0457e-01,\n",
      "         -1.7720e+00, -1.6334e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          1.2424e+00,  3.5244e-02,  8.5410e-01,  9.8925e-01, -1.1654e-01,\n",
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      "          9.3998e-01, -2.7778e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-3.6562e+00, -1.1574e-01, -2.1884e-01, -1.3110e-01,  2.4937e-01,\n",
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      "         -1.2283e+00,  1.2333e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-8.3162e-01,  1.6725e+00, -1.7862e+00,  6.7560e-02,  2.2704e-01,\n",
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      "         -8.5020e-01, -3.2333e+00, -2.0240e-01,  9.3375e-01, -1.2666e-01,\n",
      "         -9.7253e-01,  2.5498e-02,  1.5446e+00, -9.5759e-02, -1.4613e-01,\n",
      "         -1.5356e+00,  2.2637e-01, -1.6721e-01, -4.6420e-01,  1.8198e+00,\n",
      "         -6.6414e-01,  4.9193e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -1.1954e+00, -7.9258e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -1.8771e+00,  8.4180e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-1.3555e-01,  8.5813e-01, -5.6939e-01,  3.1924e-01, -1.2516e+00,\n",
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      "          6.1090e-01, -8.7937e-02]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 4.0270e-01,  3.7698e-01,  5.2756e-01,  1.5337e+00,  1.5396e+00,\n",
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      "         -7.6004e-02,  1.0062e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 0.6873,  1.5208,  0.8701, -0.2255, -0.2065,  1.0072, -0.0570,  0.0931,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          1.2214e+00, -5.1790e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -2.1326e+00, -8.2774e-01, -8.0496e-01,  1.7706e+00,  6.9510e-01,\n",
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      "         -6.5589e-01,  1.0058e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -0.0411,  0.2241, -1.1814, -0.2136,  0.9565,  1.1466, -0.3869, -0.8406,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -0.7718, -0.3250,  0.2358,  1.6416,  1.0299, -2.0768, -0.5006,  0.3635]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 0.5793, -0.8027,  0.3189, -1.0074,  0.8477, -1.0264, -0.3815,  1.2464,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -1.5828, -0.4675,  0.6266, -1.5846,  0.3976, -0.0352, -0.7197,  0.3648]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          1.1127e+00,  9.3680e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          3.2145e-01, -1.2232e-01,  2.8084e-01,  5.2085e-01,  2.3796e-01,\n",
      "          1.8971e-01,  1.3807e+00,  3.3243e-01, -2.8168e+00, -1.3974e+00,\n",
      "          1.2354e-01, -1.2698e+00,  9.6860e-01,  7.6339e-01, -5.3578e-01,\n",
      "          6.5060e-01, -2.3892e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-3.2575e-01,  1.2244e-01,  6.4177e-01, -2.3613e+00, -3.1391e-01,\n",
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      "          2.6696e-01, -1.2411e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
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    {
     "name": "stdout",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -2.5979e+00, -5.8256e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -3.7596e-01, -1.7893e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          2.0948e+00,  3.9523e-02]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -1.0580e+00, -6.7477e-01, -2.1837e-01,  1.4738e+00, -7.1650e-01,\n",
      "         -5.1523e-01, -2.6835e-01, -3.2008e-01,  1.9607e+00,  1.4887e-02,\n",
      "         -5.7113e-01,  4.6666e-01, -5.4997e-01, -3.3476e-01, -5.5540e-02,\n",
      "          5.0078e-01, -8.1465e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "center_features: tensor([[ 2.4940e-01, -8.5987e-01,  6.9422e-01,  3.3332e-01, -2.7520e-01,\n",
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      "center_features: tensor([[ 6.9817e-02,  1.4198e+00, -1.6337e+00,  1.0305e+00,  3.7708e-01,\n",
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      "         -1.3878e+00,  2.8389e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-3.5777e-01,  1.2844e-01,  7.8547e-01, -2.8238e-01, -4.2776e-02,\n",
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      "         -6.0186e-01,  4.4483e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "center_features: tensor([[-2.1679e+00,  9.8217e-01,  4.5446e-01, -4.3617e-01,  4.5683e-01,\n",
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      "         -2.0286e+00, -1.0138e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "center_features: tensor([[ 1.5886, -1.3516,  0.4060,  0.7277, -2.5834,  0.9992,  1.1699,  0.0156,\n",
      "          0.4344,  0.6397, -0.1052,  0.0599, -0.0374,  0.4606, -1.6155, -1.2900,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "center_features: tensor([[ 0.5985, -0.5246,  0.1165, -0.3514,  2.7135,  0.8978, -1.6440,  0.1088,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 1.0504, -0.4606, -0.8570,  0.3522,  0.9746, -0.8351, -1.2879, -0.0301,\n",
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      "          1.2058, -2.5780,  1.2357,  0.9245,  0.3841,  0.7534,  1.4714,  0.8616]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          5.3410e-01, -7.5968e-01,  9.8557e-01,  1.6397e-01, -6.7450e-01,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "center_features: tensor([[-1.9861e+00, -7.0554e-01, -1.0723e+00, -2.2006e+00,  1.8789e+00,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          0.5580,  0.2979, -0.4868, -1.0227, -2.3891,  0.8007, -0.1383, -0.7292]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -1.4243e+00, -3.3918e-01,  1.4616e+00,  8.6236e-01, -2.0280e-04,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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    {
     "name": "stdout",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-3.0425e+00,  1.6327e+00,  4.0968e-01, -7.5039e-01,  2.3727e-01,\n",
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      "          1.5742e+00, -7.5285e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          9.3844e-02, -1.3202e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "         -6.8438e-01,  3.0138e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         -0.4326,  0.8793,  0.5127, -1.3428,  0.3217,  0.3409, -0.3179, -0.0753,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 1.4827e+00,  1.4360e-01, -5.4483e-01,  1.3979e+00,  1.1181e+00,\n",
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      "          1.2134e-01,  3.3452e-03]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
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      "          2.8615e-01,  5.9384e-02]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-5.4213e-01, -7.4011e-02,  1.3269e-03, -2.4835e+00,  8.1754e-01,\n",
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      "         -3.9610e-01,  1.3226e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-0.4044, -0.8148,  0.1213,  0.2903, -0.2386,  1.3392,  0.2376, -0.2778,\n",
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      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 3.4907e-01,  1.6750e+00, -2.9950e-01,  1.4776e+00,  8.0543e-01,\n",
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      "          4.8080e-01, -6.3574e-01]], device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "center_features: tensor([[-1.9377e+00,  6.0297e-01,  3.7844e-01, -8.9752e-01,  2.5983e-01,\n",
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      "          5.7449e-01,  7.8342e-01,  3.9826e-01,  3.2789e-01,  2.5101e-01,\n",
      "         -3.6815e-01,  1.4839e-01,  9.9930e-01,  8.9326e-01,  5.7743e-01,\n",
      "         -1.5001e-01, -6.5180e-01, -2.3418e+00,  5.3604e-01, -2.6044e+00,\n",
      "          4.2174e-01,  1.8846e+00,  1.6248e-01, -8.0904e-02,  1.0571e+00,\n",
      "          1.1031e+00, -5.6585e-01, -8.3244e-02, -4.3930e-01, -2.4876e+00,\n",
      "         -2.5280e-02, -2.3294e-01, -2.1824e+00,  5.6182e-01,  7.3716e-01,\n",
      "          1.1219e-01,  8.9782e-01,  4.8241e-01,  5.8525e-01, -5.0936e-02,\n",
      "          1.3096e-01,  3.3238e-01,  1.9218e-01,  1.0583e+00, -3.5335e-02,\n",
      "          1.9038e-02, -3.3453e-01,  3.3441e-01, -2.6653e+00,  2.8747e-01,\n",
      "         -4.6282e-01,  1.1356e-01, -3.0007e+00,  6.0781e-01, -1.8274e+00,\n",
      "         -1.3589e-01,  4.8156e-01,  5.5224e-01,  6.7076e-02, -2.2009e-01,\n",
      "          1.5617e+00,  2.4688e+00,  2.6379e-01, -4.2663e-01,  1.1519e+00,\n",
      "          7.6013e-01, -6.4252e-01,  8.7717e-01, -7.2833e-01, -1.7419e+00,\n",
      "         -5.1474e-01,  9.7208e-02,  1.0390e-01, -3.4127e+00,  7.2904e-01,\n",
      "          4.7752e-02,  1.3302e+00, -2.7908e-01,  2.8833e-01, -3.4656e-01,\n",
      "         -9.3756e-01,  8.5324e-01, -1.9039e-01,  4.2324e-01,  5.8890e-02,\n",
      "         -8.2433e-01,  9.6712e-01, -9.9327e-01,  1.2727e+00, -1.9684e-01,\n",
      "         -9.9979e-02, -5.9072e-01,  1.5560e-01, -4.8693e-01, -3.0373e-01,\n",
      "         -2.4503e-01,  1.0837e+00,  1.1434e+00, -1.8854e+00, -4.9960e-01,\n",
      "          6.7644e-01,  1.2591e-01,  2.1726e-01,  2.6593e+00, -1.2012e+00,\n",
      "         -1.7592e+00, -1.1997e+00, -4.8310e-01,  8.4722e-01, -1.5457e-01,\n",
      "          4.0946e-01, -4.8858e-01, -7.9202e-01,  8.0291e-02,  2.7305e-01,\n",
      "          5.3177e-01,  2.9483e-01,  1.0250e+00,  6.0808e-04,  9.3618e-01,\n",
      "         -5.1078e-01, -1.6266e+00]], device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-0.0961, -1.1469, -0.6998, -0.0811,  2.1302,  0.6134, -1.0252,  0.8996,\n",
      "         -0.6957, -0.0298,  0.4501, -0.4948, -2.2468,  1.0362, -0.6406, -0.3956,\n",
      "          0.0130, -0.5138,  0.4056, -0.0601, -0.2697,  0.0683, -0.8105,  0.5285,\n",
      "         -2.0372,  0.1571, -0.1633, -1.1638, -0.0202,  0.0871, -0.2530,  0.0759,\n",
      "         -0.3560,  0.8162,  3.0155,  1.5345, -0.2067, -1.7520,  0.7607,  0.1200,\n",
      "         -1.1472,  0.3139,  0.9802,  0.8306,  0.1711, -0.8193,  0.0800, -0.0261,\n",
      "          0.7228,  2.3866, -1.8581, -0.4691,  1.3531, -1.2914,  0.3171, -0.1222,\n",
      "         -0.9345,  2.0543,  0.3097, -0.8728, -0.1534, -0.0074,  1.1268, -0.4983,\n",
      "          0.3492, -1.0173, -0.7252,  0.4101,  1.7205, -1.3789, -0.3536,  0.0086,\n",
      "          0.0888, -1.4687, -0.2277, -0.7560,  1.6852, -0.2330,  0.6183,  1.6595,\n",
      "         -0.6926, -0.5308, -1.3909,  0.1432, -0.0232, -0.0866, -0.9272, -0.1979,\n",
      "          1.6353,  0.5281,  0.1665,  0.5584,  2.6166, -0.4638,  2.8949, -0.0606,\n",
      "         -0.0496, -0.2396,  0.5158, -0.3447,  0.9154, -0.3588, -0.9223, -1.8242,\n",
      "         -1.2378, -0.5861,  1.2425,  1.0627, -0.2747,  0.6821,  0.1000, -0.9769,\n",
      "         -0.9985,  0.4950,  0.8262, -0.0160, -1.3517, -1.1394, -0.4160,  0.3699,\n",
      "         -0.1211, -0.3759, -0.0267,  0.3061, -1.8339,  2.2841,  0.1252, -0.3806,\n",
      "         -0.7386, -0.0487,  0.0503, -2.2594,  0.0514,  0.5939, -0.0586, -0.1433,\n",
      "          0.4847,  1.1923, -0.4586,  0.3577, -1.7478,  0.5490,  0.8929, -0.3758,\n",
      "         -0.3821,  1.3308,  1.1869,  0.3805, -0.1632,  0.0935, -0.1579,  0.8213,\n",
      "          0.3597,  1.3454, -0.4013, -2.4046,  1.1571, -0.2023,  0.5409,  1.8861,\n",
      "         -0.4827,  0.5742, -0.5244, -1.9007,  0.4562,  0.1600,  1.2193,  1.1984,\n",
      "         -2.3750,  0.5673,  0.1653, -1.4342,  0.6984,  0.7786,  0.4227,  0.9131,\n",
      "         -1.2547, -0.6713,  0.9069, -1.3452, -1.1658, -1.6215, -0.3078, -0.4512,\n",
      "          0.1160,  0.1001,  0.8366,  0.3193, -1.6413,  1.6766,  0.9673, -0.6329]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[ 0.3459,  0.5768, -0.9408, -0.6627,  0.3323,  1.3135, -0.4149,  0.1205,\n",
      "          1.0420,  0.2673,  0.2032,  0.2408, -1.8985,  0.4276, -1.2857, -1.1633,\n",
      "          1.1021, -0.2639,  0.7165, -1.5679, -0.0064,  0.4884, -0.0263, -0.4578,\n",
      "         -1.7823, -0.3183, -0.4127, -0.1078,  0.7557,  0.1633, -1.4354, -0.2245,\n",
      "         -1.1331,  0.2426, -0.0105, -0.7057,  0.0297, -0.5492,  1.9907,  0.2530,\n",
      "         -0.3549,  0.4493,  0.2918, -1.3323,  0.5998,  0.2720, -0.3349,  2.3740,\n",
      "         -0.1357,  0.5613,  0.1478, -0.2404,  0.7295,  3.0792,  0.5495, -0.9354,\n",
      "         -2.7762, -1.0329,  1.9031, -0.8386,  0.6661,  1.0919,  0.4642, -0.4431,\n",
      "          0.6875,  0.1025, -0.7506,  0.9185, -0.1493, -0.4655, -0.0167, -0.7440,\n",
      "         -0.8729,  0.3904,  0.6167,  1.2398,  1.2744, -0.0403,  0.7520, -0.8532,\n",
      "         -0.0365,  0.3908,  0.0707, -0.3478, -0.2746,  0.1747, -0.7987, -1.5814,\n",
      "          0.7717, -1.7088, -1.2758, -0.0372,  0.6422, -1.2762,  0.7240,  0.6151,\n",
      "         -0.0133, -0.2382, -0.2444, -1.0757, -0.5779, -1.1793,  0.1357,  0.0136,\n",
      "         -2.9218,  0.1619,  1.5724,  1.5230,  0.8797, -1.4488,  0.9646,  1.2057,\n",
      "          0.5899,  0.0962,  2.6770,  1.2322,  0.2956,  0.1604, -0.3745,  0.3287,\n",
      "          0.2746, -1.0151, -2.5921,  1.6713,  0.5808, -0.0537,  0.5311, -1.3014,\n",
      "          0.0044, -0.1394, -1.2678,  0.3516,  0.0633, -1.3053, -2.7959,  0.5571,\n",
      "         -0.3798,  0.1594, -0.5779,  2.2882, -0.4282,  0.8749,  0.4244, -0.4194,\n",
      "         -0.1753,  0.1441, -0.6713, -0.5866,  0.1813, -1.5163,  0.0139, -1.0625,\n",
      "         -0.1764, -0.2466,  0.2599, -0.0344, -0.9927, -0.9586,  1.1313, -0.1452,\n",
      "         -0.0245, -1.8428, -0.8413, -0.6064,  1.9088,  0.1176,  1.0064, -0.4272,\n",
      "          0.2688,  1.5599,  1.5747,  0.6800,  1.3377, -1.9712,  0.3751,  0.2614,\n",
      "          0.1760, -0.4159,  0.3648,  1.6394,  1.3404, -0.3257,  2.1561,  0.1434,\n",
      "         -0.5048,  0.2084,  1.2935, -0.1027,  0.0066, -1.8942, -0.9892,  0.9523]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n",
      "center_features: tensor([[-0.3761,  0.2553, -0.1772,  1.1519, -0.9959, -0.5134, -0.7693,  0.2995,\n",
      "          0.8666,  0.8230,  0.6926, -0.0414, -0.9932,  0.0959, -0.5768,  0.3040,\n",
      "          0.6010,  0.2622,  0.0821,  0.1001,  0.4881,  0.4420, -1.4653, -0.9390,\n",
      "         -1.9926,  3.0555,  0.0262,  1.3963,  2.9077,  0.2009, -0.6391, -1.7383,\n",
      "         -1.8801,  0.0948, -0.0215, -0.3304,  0.3083,  0.2700, -0.3702,  0.7097,\n",
      "          1.9483,  0.2404, -0.8667,  0.1634,  0.2453,  1.6122,  0.2549, -0.2355,\n",
      "         -0.4857,  0.3170,  0.2314,  0.0721,  0.0751,  0.1331, -0.0456, -2.6269,\n",
      "         -1.6194,  0.4628, -1.4379, -0.7305,  0.7765,  0.1653,  0.4972, -0.7611,\n",
      "          0.6418,  0.2633, -0.8180, -0.6827, -0.9246, -0.2795,  0.9193,  2.0415,\n",
      "         -1.2068,  0.6947, -0.5416,  0.9886, -0.9748, -0.7975,  0.0273, -0.7160,\n",
      "          0.0102, -1.0871, -1.0823,  2.0888,  1.1539,  0.7757, -0.8148, -0.9445,\n",
      "          0.0145,  1.0739,  0.5638, -0.5655,  0.7577,  0.3476, -0.0102,  0.6661,\n",
      "         -1.2566, -0.9344, -0.0888,  0.0952, -2.2276, -0.3792,  0.3281, -1.6210,\n",
      "         -0.9614,  0.6638, -0.4765, -0.9351, -0.0638, -0.5747,  0.3063,  0.6755,\n",
      "          0.9094,  0.7617,  1.4834,  0.2303, -1.8621,  2.2542, -0.4826,  1.3099,\n",
      "         -0.9339,  0.1371,  0.9504,  1.7807, -1.6181,  0.9325,  0.9379, -0.9241,\n",
      "          0.7138, -0.0277,  0.4112,  0.5946, -0.8010, -1.7468,  0.3058,  0.1217,\n",
      "          0.9953,  2.5247, -0.2211,  0.4518,  1.8093, -0.5663,  1.1294,  0.0543,\n",
      "         -0.9654, -0.2783, -0.0706, -0.2466,  0.5241, -0.6893, -2.0506, -0.6472,\n",
      "         -2.0312,  0.2599,  1.4487, -1.9389, -1.0809,  0.7361,  1.1842,  1.0075,\n",
      "          0.3252, -1.4149, -0.7155,  0.5950, -0.2221, -0.9331, -0.1004, -0.0670,\n",
      "         -0.0357, -1.0920,  0.7965,  0.6715, -1.9619, -0.2076,  1.2988,  0.3339,\n",
      "         -0.7054,  1.4734, -2.4614, -0.5887,  0.3397,  0.3212,  1.1178,  0.8276,\n",
      "         -0.9202,  0.8011,  0.0735, -0.5514,  0.5308,  0.7239,  0.3951,  0.4415]],\n",
      "       device='cuda:0', grad_fn=<IndexBackward0>)\n"
     ]
    }
   ],
   "source": [
    "bsne_model.train()\n",
    "optimizer.zero_grad()\n",
    "\n",
    "# 采样中心节点\n",
    "batch_centers = rd.sample(range(feat_data.shape[0]), args['batch_size'])\n",
    "\n",
    "# 计算TSNE损失\n",
    "#     tsne_loss = calculate_tsne_loss(\n",
    "#         model=bsne_model,\n",
    "#         feat_data=feat_data,\n",
    "#         dist_matrix=dist_matrix,\n",
    "#         batch_centers=batch_centers,\n",
    "#         sample_size=args['sample_size'],\n",
    "#         temperature=100,\n",
    "#         eps=1e-10,\n",
    "#         edge_indexs=edge_indexs,\n",
    "#         device=device\n",
    "#     )\n",
    "\n",
    "\n",
    "sample_size=args['sample_size']\n",
    "temperature=100\n",
    "eps=1e-10\n",
    "\n",
    "\n",
    "total_loss = 0.0\n",
    "batch_size = len(batch_centers)\n",
    "\n",
    "# 为每个中心节点构建Bp子图\n",
    "bp_subgraphs = []\n",
    "for xi in batch_centers:\n",
    "    # 根据距离矩阵计算采样概率\n",
    "    dist_row = dist_matrix[xi].cpu().numpy()\n",
    "    probs = np.exp(-dist_row)\n",
    "    probs[xi] = 0  # 排除自身\n",
    "    probs /= probs.sum()\n",
    "\n",
    "    # 采样邻居节点\n",
    "    available_nodes = len(dist_row) - 1\n",
    "    actual_sample_size = min(sample_size, available_nodes)\n",
    "\n",
    "    if actual_sample_size > 0:\n",
    "        neighbors = np.random.choice(len(dist_row), size=actual_sample_size, p=probs, replace=False)\n",
    "        # 将中心节点和采样的邻居合并\n",
    "        subgraph_nodes = [xi] + neighbors.tolist()\n",
    "    else:\n",
    "        # 如果没有可用邻居，只使用中心节点\n",
    "        subgraph_nodes = [xi]\n",
    "\n",
    "    bp_subgraphs.append(subgraph_nodes)\n",
    "\n",
    "# 为每个中心节点构建Bu子图（均匀采样）\n",
    "bu_subgraphs = []\n",
    "for _ in range(batch_size):\n",
    "    # 随机采样节点构建Bu子图 (sample_size + 1个节点)\n",
    "    random_nodes = np.random.choice(feat_data.shape[0], size=sample_size + 1, replace=False)\n",
    "    bu_subgraphs.append(random_nodes.tolist())\n",
    "\n",
    "# 计算Bp子图中所有节点的特征\n",
    "bp_features_batch = []\n",
    "for bp_nodes in bp_subgraphs:\n",
    "    bp_node_features = []\n",
    "\n",
    "    # 为子图中的每个节点计算特征\n",
    "    for node_idx in bp_nodes:\n",
    "        # 构建该节点的一阶邻居子图\n",
    "        node_subgraph = create_node_subgraph(node_idx, feat_data, edge_indexs, device)\n",
    "\n",
    "        # 获取节点特征\n",
    "        _, node_feature = bsne_model([node_subgraph])\n",
    "\n",
    "        bp_node_features.append(node_feature.squeeze(0))\n",
    "\n",
    "    # 将该Bp子图所有节点的特征收集起来\n",
    "    bp_features_batch.append(torch.stack(bp_node_features))  # [sample_size+1, d_model]\n",
    "\n",
    "# 计算Bu子图中所有节点的特征\n",
    "bu_features_batch = []\n",
    "for bu_nodes in bu_subgraphs:\n",
    "    bu_node_features = []\n",
    "\n",
    "    # 为子图中的每个节点计算特征\n",
    "    for node_idx in bu_nodes:\n",
    "        # 构建该节点的一阶邻居子图\n",
    "        node_subgraph = create_node_subgraph(node_idx, feat_data, edge_indexs, device)\n",
    "\n",
    "        # 获取节点特征\n",
    "        _, node_feature = bsne_model([node_subgraph])\n",
    "\n",
    "        bu_node_features.append(node_feature.squeeze(0))\n",
    "\n",
    "    # 将该Bu子图所有节点的特征收集起来\n",
    "    bu_features_batch.append(torch.stack(bu_node_features))  # [sample_size+1, d_model]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e662295a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "local_loss: 1.2640038480145315,global_loss: -8.400518329968603\n",
      "local_loss: 1.13071560804573,global_loss: -8.4061614853744\n",
      "local_loss: 1.044974850034036,global_loss: -8.70820760802065\n",
      "local_loss: 1.2717956638008783,global_loss: -8.508699119433288\n",
      "local_loss: 1.3386180792398488,global_loss: -8.818213194915494\n",
      "local_loss: 0.6167296699219122,global_loss: -8.695511210761559\n",
      "local_loss: 0.9149223558548447,global_loss: -8.645041293626116\n",
      "local_loss: 0.45657774467750384,global_loss: -8.62582781704504\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3800348/1668774860.py:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  Q = torch.clamp(torch.tensor(Q, device=device), min=eps)\n"
     ]
    }
   ],
   "source": [
    "# 针对每一对Bp和Bu子图计算loss\n",
    "for i in range(batch_size):\n",
    "    bp_nodes = bp_subgraphs[i]\n",
    "    bp_features = bp_features_batch[i]  # [bp_size, d_model]\n",
    "    bu_features = bu_features_batch[i]  # [bu_size, d_model]\n",
    "\n",
    "    # 计算Bp子图中的距离矩阵（原始图距离）\n",
    "    dist_bp = dist_matrix[bp_nodes][:, bp_nodes].cpu().numpy()\n",
    "\n",
    "    # 计算P矩阵（基于距离）\n",
    "    P = (1.0 + dist_bp ** 2) ** -1\n",
    "    np.fill_diagonal(P, 0)  # 对角线置零\n",
    "    P = (P + P.T) / 2  # 对称化\n",
    "    row_sums = P.sum(axis=1, keepdims=True)\n",
    "    row_sums = np.where(row_sums == 0, eps, row_sums)\n",
    "    P = P / row_sums\n",
    "    P = torch.clamp(torch.tensor(P, device=device), min=eps)\n",
    "\n",
    "    # 计算Q矩阵（基于特征距离）\n",
    "    pairwise_dist = torch.cdist(bp_features, bp_features, p=2)  # [bp_size, bp_size]\n",
    "    Q = (1.0 + pairwise_dist ** 2 / temperature) ** -1\n",
    "    Q.fill_diagonal_(0)\n",
    "    Q = (Q + Q.permute(1, 0)) / 2\n",
    "    Q_sum = Q.sum(dim=1, keepdim=True)\n",
    "    Q_sum = torch.where(Q_sum == 0, torch.tensor(eps, device=device), Q_sum)\n",
    "    Q = Q / Q_sum\n",
    "    Q = torch.clamp(torch.tensor(Q, device=device), min=eps)\n",
    "    \n",
    "\n",
    "    # 局部损失\n",
    "    loss_local = (P * (torch.log(P) - torch.log(Q))).sum()\n",
    "\n",
    "    # 全局损失\n",
    "    sum_p_sub = P.sum(dim=1)\n",
    "    N = dist_matrix.shape[0]\n",
    "    k_Bp = (sum_p_sub * N / len(bp_nodes)).mean()\n",
    "\n",
    "    # 计算Bp子图与Bu子图之间的特征距离\n",
    "    sum_e_bu = torch.exp(-torch.cdist(bp_features, bu_features, p=2)).sum()\n",
    "    sum_e_bp = torch.exp(-pairwise_dist).sum()\n",
    "\n",
    "    global_ratio = k_Bp * (sum_e_bu / (sum_e_bp + eps))\n",
    "    loss_global = torch.log(global_ratio.clamp(min=eps))\n",
    "\n",
    "    # 合并局部损失和全局损失\n",
    "    total_loss += (loss_local + loss_global)\n",
    "    print(f\"local_loss: {loss_local.item()},global_loss: {loss_global.item()}\")\n",
    "bsne_loss = total_loss / batch_size\n",
    "\n",
    "\n",
    "# bsne_loss.backward()\n",
    "# optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2898a393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-60.7698, device='cuda:0', dtype=torch.float64, grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "25bfd82c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[        inf,  6.3849e-03, -5.6553e-03, -1.3159e-03,  3.0113e-03,\n",
       "         -2.5004e-03,  4.0822e-03,  1.4097e-03, -3.0114e-03,  1.2724e-03,\n",
       "          2.8112e-04,  2.2298e-03,  9.4685e-04, -7.3233e-03,  1.4504e-03,\n",
       "          4.8205e-04, -2.0718e-04,  3.2238e-03,  6.9765e-04, -3.4736e-03,\n",
       "          7.4209e-05],\n",
       "        [ 5.9543e-03,         inf,  5.4432e-03,  1.3217e-03, -3.4293e-04,\n",
       "          1.5998e-03, -3.7286e-03, -2.2887e-03, -4.1469e-04,  3.9923e-03,\n",
       "          5.4067e-03,  4.2893e-03,  5.7809e-03, -3.8517e-05, -2.3809e-05,\n",
       "         -1.6434e-02, -4.6678e-04, -4.9564e-03, -6.2074e-03,  6.9324e-03,\n",
       "          3.7655e-03],\n",
       "        [-4.9013e-03,  6.9365e-03,         inf, -5.0907e-03,  1.3411e-04,\n",
       "          2.8424e-03,  3.1476e-03, -1.5228e-02,  4.6097e-03, -3.1062e-03,\n",
       "          6.8437e-03,  4.0398e-03,  2.5945e-03, -8.4924e-04,  7.6343e-04,\n",
       "         -1.1095e-04,  3.2035e-03,  2.4711e-03, -1.5299e-03,  1.7238e-03,\n",
       "         -6.2393e-04],\n",
       "        [ 5.9244e-04,  3.8580e-03, -4.0477e-03,         inf,  4.9669e-03,\n",
       "         -1.7192e-03,  6.0548e-03,  5.4768e-03, -2.9218e-03, -2.4311e-03,\n",
       "          6.6601e-03,  4.4046e-03,  1.8110e-03, -1.5306e-03,  4.5510e-03,\n",
       "         -1.7025e-02, -2.4516e-03,  2.1323e-03, -2.9246e-03,  3.3035e-03,\n",
       "          8.7074e-04],\n",
       "        [ 6.9129e-03,  3.9224e-03,  2.8512e-03,  6.8459e-03,         inf,\n",
       "          3.0983e-03,  1.4249e-03, -1.3449e-02,  6.2128e-03,  4.3173e-03,\n",
       "          2.6001e-03, -1.9080e-02,  3.6952e-03, -2.6144e-03,  1.2155e-03,\n",
       "         -1.5732e-02,  6.0494e-03,  1.0956e-02,  1.0324e-02,  9.3438e-04,\n",
       "          2.0197e-03],\n",
       "        [-1.4858e-03,  3.2728e-03,  3.0222e-03, -2.5825e-03,  5.4820e-04,\n",
       "                 inf,  3.9547e-03, -3.5084e-03,  7.2245e-04,  2.9892e-04,\n",
       "          2.1933e-03, -1.7660e-02,  5.0569e-03, -2.2378e-03, -5.7199e-04,\n",
       "          2.4756e-03,  2.6184e-03,  3.2990e-03,  1.5207e-03,  3.2353e-03,\n",
       "          5.0754e-03],\n",
       "        [ 2.4102e-03, -4.7280e-03,  5.2043e-04,  2.3379e-03, -3.7371e-03,\n",
       "          1.1322e-03,         inf,  5.7586e-03, -2.5614e-03, -2.9036e-03,\n",
       "         -8.3872e-04,  5.6938e-03,  2.8215e-03, -2.8923e-03, -5.1220e-03,\n",
       "         -2.9444e-03, -3.9651e-04,  1.4405e-03,  5.8792e-03,  2.8388e-03,\n",
       "         -2.4018e-03],\n",
       "        [ 7.8323e-03,  4.3331e-03, -1.4359e-02,  1.0160e-02, -1.3040e-02,\n",
       "          1.1099e-03,  1.4713e-02,         inf,  1.0517e-02,  3.2865e-03,\n",
       "          2.1331e-03, -1.5271e-02,  6.1338e-03,  6.2273e-03,  5.0733e-03,\n",
       "         -1.4671e-02,  1.2563e-02,  6.1028e-03, -9.0934e-04, -1.5010e-02,\n",
       "          1.3219e-02],\n",
       "        [-1.4047e-03,  1.8635e-03,  5.3946e-03, -3.1799e-03,  4.1082e-03,\n",
       "          1.3276e-03,  7.7163e-04,  5.5387e-03,         inf, -2.1688e-03,\n",
       "          2.0971e-03,  3.3097e-03,  4.4541e-04, -5.6658e-03, -7.1098e-04,\n",
       "         -1.7314e-02,  5.9658e-03,  4.0830e-03, -9.8696e-05, -7.9753e-04,\n",
       "          6.1268e-03],\n",
       "        [ 2.8865e-03,  6.0861e-03, -2.2910e-03, -2.6497e-03,  2.2244e-03,\n",
       "          8.5385e-04,  3.8252e-04, -9.5371e-04, -2.1422e-03,         inf,\n",
       "          2.0423e-04,  2.0807e-03, -9.0752e-05, -3.3204e-03,  2.0909e-04,\n",
       "         -6.2838e-03,  7.5082e-04,  1.7851e-03, -1.1048e-03, -3.0616e-04,\n",
       "          3.0654e-03],\n",
       "        [-4.2704e-03,  9.1085e-04,  8.5979e-04, -2.5268e-04, -5.4418e-03,\n",
       "         -3.5210e-03, -3.7568e-03, -7.8594e-03, -4.1571e-03, -5.8434e-03,\n",
       "                 inf, -2.8641e-03, -6.9078e-03, -3.0383e-03, -9.0211e-03,\n",
       "          1.0082e-01,  1.5821e-03, -1.1453e-03, -2.2392e-03, -5.6423e-03,\n",
       "         -5.7113e-04],\n",
       "        [ 9.7288e-03,  1.2707e-02,  1.0708e-02,  9.9301e-03, -1.9201e-02,\n",
       "         -1.7323e-02,  1.5771e-02, -1.5370e-02,  8.9703e-03,  7.6543e-03,\n",
       "          9.2147e-03,         inf,  1.3774e-02,  3.0228e-03,  9.0986e-03,\n",
       "         -1.5335e-02,  1.4099e-02,  1.3744e-02,  7.3374e-03, -1.5541e-02,\n",
       "         -1.5881e-02],\n",
       "        [-5.2191e-03, -5.3560e-04, -4.5913e-03, -6.1743e-03, -6.0298e-03,\n",
       "         -2.6130e-03, -2.0665e-03, -6.1287e-03, -7.1341e-03, -7.5762e-03,\n",
       "         -8.3311e-03, -1.0225e-03,         inf,  8.0513e-02, -1.0719e-02,\n",
       "         -1.6567e-02, -5.0169e-03, -2.9589e-03,  8.6109e-02, -7.9866e-03,\n",
       "         -1.7389e-03],\n",
       "        [-9.5251e-03, -2.5339e-03, -4.5307e-03, -6.0251e-03, -8.1724e-03,\n",
       "         -5.8899e-03, -4.1003e-03, -3.0108e-03, -9.3853e-03, -7.4018e-03,\n",
       "         -1.5884e-03, -6.1002e-03,  8.8126e-02,         inf, -3.2803e-03,\n",
       "         -1.8201e-02, -3.9384e-03, -4.0834e-03,  9.3487e-02, -7.3801e-03,\n",
       "         -2.1922e-03],\n",
       "        [ 2.5131e-03,  1.6189e-03,  9.3053e-04,  3.6064e-03, -1.1962e-03,\n",
       "         -5.4672e-04, -2.3873e-03,  5.8319e-05, -1.2723e-03, -3.4234e-04,\n",
       "         -3.7631e-03,  2.7569e-03, -4.1778e-03,  7.6486e-04,         inf,\n",
       "          6.3778e-03,  2.9687e-03,  2.2774e-03, -5.0570e-03, -1.7231e-03,\n",
       "         -1.8294e-03],\n",
       "        [ 1.8811e-02, -1.1894e-02,  1.6855e-02, -1.4310e-02, -1.2574e-02,\n",
       "          1.9984e-02,  1.6472e-02, -1.1661e-02, -1.4518e-02,  7.6377e-03,\n",
       "          2.0617e-01, -1.2322e-02, -1.1221e-02, -1.5955e-02,  2.5255e-02,\n",
       "                 inf, -1.4897e-02, -1.1481e-02, -1.3609e-02, -1.3425e-02,\n",
       "         -1.2758e-02],\n",
       "        [-3.3848e-03, -3.0691e-03, -1.0476e-03, -7.2672e-03, -1.0803e-03,\n",
       "         -1.7578e-03, -1.9727e-03,  2.0972e-03,  7.8864e-04, -4.0097e-03,\n",
       "          3.0721e-03,  2.4741e-03, -2.0690e-03, -4.1762e-03, -1.3723e-03,\n",
       "         -1.8723e-02,         inf,  1.0513e-01, -7.4730e-03, -2.6354e-03,\n",
       "         -5.8623e-03],\n",
       "        [-7.1438e-04, -7.8352e-03, -2.3249e-03, -3.6080e-03,  2.6453e-03,\n",
       "         -1.7230e-03, -8.2043e-04, -3.8834e-03, -1.5569e-03, -3.6219e-03,\n",
       "         -3.1743e-04,  1.5896e-03, -4.6131e-04, -4.9288e-03, -2.6279e-03,\n",
       "         -1.6582e-02,  1.0364e-01,         inf, -1.1732e-02,  2.8647e-04,\n",
       "         -3.3491e-04],\n",
       "        [-3.2747e-03, -8.7595e-03, -5.9999e-03, -8.1126e-03,  1.5864e-03,\n",
       "         -3.5118e-03,  2.8173e-03, -9.4409e-03, -5.4397e-03, -6.3117e-03,\n",
       "         -1.7041e-03, -3.7196e-03,  9.1524e-02,  9.1288e-02, -9.3346e-03,\n",
       "         -1.7076e-02, -7.8947e-03, -1.1313e-02,         inf, -3.2386e-03,\n",
       "         -6.6453e-03],\n",
       "        [ 3.5976e-05,  1.1725e-02,  4.6605e-03,  5.2262e-03,  1.0681e-03,\n",
       "          6.0643e-03,  8.6677e-03, -1.5245e-02,  1.1757e-03,  1.7152e-03,\n",
       "          2.5007e-03, -1.5650e-02,  1.4489e-03, -1.5171e-03,  7.7953e-04,\n",
       "         -1.6355e-02,  4.4178e-03,  8.4125e-03,  4.5894e-03,         inf,\n",
       "          7.4220e-03],\n",
       "        [ 1.4358e-03,  5.8323e-03, -2.0529e-04,  2.5824e-04, -2.3692e-04,\n",
       "          5.4596e-03,  5.8947e-04,  7.8029e-03,  5.9175e-03,  2.8854e-03,\n",
       "          5.8396e-03, -1.6709e-02,  6.4757e-03,  2.3327e-03, -1.6866e-03,\n",
       "         -1.6565e-02, -1.5987e-03,  5.1706e-03, -1.8994e-03,  4.8930e-03,\n",
       "                 inf]], device='cuda:0', dtype=torch.float64,\n",
       "       grad_fn=<MulBackward0>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P * (torch.log(P) - torch.log(Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f1a759be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(inf, device='cuda:0', dtype=torch.float64, grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(P * (torch.log(P) - torch.log(Q))).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f7cabd1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([[nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',\n",
       "        grad_fn=<StackBackward0>),\n",
       " tensor([[nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',\n",
       "        grad_fn=<StackBackward0>),\n",
       " tensor([[nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',\n",
       "        grad_fn=<StackBackward0>),\n",
       " tensor([[nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',\n",
       "        grad_fn=<StackBackward0>),\n",
       " tensor([[nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',\n",
       "        grad_fn=<StackBackward0>),\n",
       " tensor([[nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',\n",
       "        grad_fn=<StackBackward0>),\n",
       " tensor([[nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',\n",
       "        grad_fn=<StackBackward0>),\n",
       " tensor([[nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan],\n",
       "         [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',\n",
       "        grad_fn=<StackBackward0>)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bp_features_batch # [sample_size+1, d_model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6113951",
   "metadata": {},
   "outputs": [],
   "source": [
    "bp_features_batch # [sample_size+1, d_model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9caddc0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "node_subgraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11d19e13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce099272",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04587bd4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c758ff6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de77733f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec313c2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9a8ce87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ae6f647",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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